text:
approach to tame these sources of uncertainty through the introduction of "computational focus groups" to track opinion shifts in social media streams. Our approach uses prior user behaviors to detect users' biases, then groups users with similar biases together. We track the behavior streams from these like-minded sub-groups and present time-dependent collective measures of their opinions. These measures control for the response rate and base attitudes of the users, making shifts in opinion both easier to detect and easier to interpret. We test the effectiveness of our system by tracking groups' Twitter responses to a common stimulus set: the 2012 U.S. presidential election debates. While our groups' behavior is consistent with their biases, there are numerous moments and topics on which they behave "out of character," suggesting precise targets for follow-up inquiry. We also demonstrate that tracking elite users with well-established biases does not yield such insights, as they are insensitive to the stimulus and simply reproduce expected patterns. The effectiveness of our system suggests a new direction both for researchers and data-driven journalists interested in identifying opinion shifting processes in real-time.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {737–748},
numpages = {12},
keywords = {process inference, social meter, public opinion, computational social science, data-driven journalism, real time system},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488454,
author = {Lindley, Si\^{a}n E. and Marshall, Catherine C. and Banks, Richard and Sellen, Abigail and Regan, Tim},
title = {Rethinking the Web as a Personal Archive},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488454},
doi = {10.1145/2488388.2488454},
abstract = {In recent years the Web has evolved substantially, transforming from a place where we primarily find information to a place where we also leave, share and keep it. This presents a fresh set of challenges for the management of personal information, which include how to underpin greater awareness and more control over digital belongings and other personally meaningful content that is hosted online. In the study reported here, we follow up on research that suggests a sense of ownership and control can be reinforced by federating online content as a virtual, single store; we do this by conducting interviews with 14 individuals about their Web-based content. Participants were asked to give the researchers a tour of online content that is personally meaningful to them; to perform a search for themselves in order to uncover additional content; and to respond to a series of design envisionments. We examine whether there is any value in an integrated personal archive that would automatically update and serve firstly, as a source of information regarding the content within it (e.g. where it is stored, who has the rights to it), and secondly, as a resource for crafting personal artefacts such as scrapbooks, CVs and gifts for others. Our analysis leads us to reject the concept of a single archive. Instead, we present a framework of five different types of online content, each of which has separate implications for personal information management.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {749–760},
numpages = {12},
keywords = {cloud, virtual possession, personal information management, control, social media, ownership},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488455,
author = {Liptchinsky, Vitaliy and Satzger, Benjamin and Zabolotnyi, Rostyslav and Dustdar, Schahram},
title = {Expressive Languages for Selecting Groups from Graph-Structured Data},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488455},
doi = {10.1145/2488388.2488455},
abstract = {Many query languages for graph-structured data are based on regular path expressions, which describe relations among pairs of nodes. We propose an extension that allows to retrieve groups of nodes based on group structural characteristics and relations to other nodes or groups. It allows to express group selection queries in a concise and natural style, and can be integrated into any query language based on regular path queries. We present an efficient algorithm for evaluating group queries in polynomial time from an input data graph. Evaluations using real-world social networks demonstrate the practical feasibility of our approach.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {761–770},
numpages = {10},
keywords = {semantic web, graph query languages, social selection},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488456,
author = {Liu, Han and Nazir, Atif and Joung, Jinoo and Chuah, Chen-Nee},
title = {Modeling/Predicting the Evolution Trend of Osn-Based Applications},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488456},
doi = {10.1145/2488388.2488456},
abstract = {While various models have been proposed for generating social/friendship network graphs, the dynamics of user interactions through online social network (OSN) based applications remain largely unexplored. We previously developed a growth model to capture static weekly snapshots of user activity graphs (UAGs) using data from popular Facebook gifting applications. This paper presents a new continuous graph evolution model aimed to capture microscopic user-level behaviors that govern the growth of the UAG and collectively define the overall graph structure. We demonstrate the utility of our model by applying it to forecast the number of active users over time as the application transitions from initial growth to peak/mature and decline/fatique phase. Using empirical evaluations, we show that our model can accurately reproduce the evolution trend of active user population for gifting applications, or other OSN applications that employ similar growth mechanisms. We also demonstrate that the predictions from our model can guide the generation of synthetic graphs that accurately represent empirical UAG snapshots sampled at different evolution stages.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {771–780},
numpages = {10},
keywords = {applications, social games, social gifting, facebook, online social networks, algorithms},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488457,
author = {Liu, Xin and Aberer, Karl},
title = {SoCo: A Social Network Aided Context-Aware Recommender System},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488457},
doi = {10.1145/2488388.2488457},
abstract = {Contexts and social network information have been proven to be valuable information for building accurate recommender system. However, to the best of our knowledge, no existing works systematically combine diverse types of such information to further improve recommendation quality. In this paper, we propose SoCo, a novel context-aware recommender system incorporating elaborately processed social network information. We handle contextual information by applying random decision trees to partition the original user-item-rating matrix such that the ratings with similar contexts are grouped. Matrix factorization is then employed to predict missing preference of a user for an item using the partitioned matrix. In order to incorporate social network information, we introduce an additional social regularization term to the matrix factorization objective function to infer a user's preference for an item by learning opinions from his/her friends who are expected to share similar tastes. A context-aware version of Pearson Correlation Coefficient is proposed to measure user similarity. Real datasets based experiments show that SoCo improves the performance (in terms of root mean square error) of the state-of-the-art context-aware recommender system and social recommendation model by 15.7% and 12.2% respectively.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {781–802},
numpages = {22},
keywords = {context-awareness, recommender system, social networks, matrix factorization},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488458,
author = {Liu, Yefeng and Alexandrova, Todorka and Nakajima, Tatsuo},
title = {Using Stranger as Sensors: Temporal and Geo-Sensitive Question Answering via Social Media},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488458},
doi = {10.1145/2488388.2488458},
abstract = {MoboQ is a location-based real-time social question answering service deployed in the field in China. Using MoboQ, people can ask temporal and geo-sensitive questions, such as how long is the line at a popular business right now, and then receive answers that crowdsourced from other users in a timely fashion. To obtain answers for questions, the system analyzes the live stream from public microblogging service Sina Weibo to identify people who are likely to currently be at the place that is associated with a question and sends them the unsolicited question through the microblogging service from which they were identified. MoboQ was deployed in China at the beginning of 2012, until October of the same year, it was used to ask 15,224 questions by 35,214 registered users, and it gathered 29,491 answers; 74.6% of the questions received at least one answer, 28% received a first response within 10 minutes, and 51% of the questions got first answer within 20 minutes. In total, 91% of the questions successfully found at least one answer candidate, and they were sent to 162,954 microblogging service users. We analyze the usage patterns and behaviors of the real-world end-users, discuss the lessons learned, and outline the future directions and possible applications that could be built on top of MoboQ.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {803–814},
numpages = {12},
keywords = {social media, microblogging, temporal and geo-sensitive question answering, strangersourcing, human as sensors, crowdsourcing},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488459,
author = {Lo, James Teng Kin and Wohlstadter, Eric and Mesbah, Ali},
title = {Imagen: Runtime Migration of Browser Sessions for Javascript Web Applications},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488459},
doi = {10.1145/2488388.2488459},
abstract = {Due to the increasing complexity of web applications and emerging HTML5 standards, a large amount of runtime state is created and managed in the user's browser. While such complexity is desirable for user experience, it makes it hard for developers to implement mechanisms that provide users ubiquitous access to the data they create during application use. This paper presents our research into browser session migration for JavaScript-based web applications. Session migration is the act of transferring a session between browsers at runtime. Without burden to developers, our system allows users to create a snapshot image that captures all runtime state needed to resume the session elsewhere. Our system works completely in the JavaScript layer and thus snapshots can be transfered between different browser vendors and hardware devices. We report on performance metrics of the system using five applications, four different browsers, and three different devices.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {815–826},
numpages = {12},
keywords = {json, dom, session migration, html5, javascript},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488460,
author = {Lou, Jing-Kai and Park, Kunwoo and Cha, Meeyoung and Park, Juyong and Lei, Chin-Laung and Chen, Kuan-Ta},
title = {Gender Swapping and User Behaviors in Online Social Games},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488460},
doi = {10.1145/2488388.2488460},
abstract = {Modern Massively Multiplayer Online Role-Playing Games (MMORPGs) provide lifelike virtual environments in which players can conduct a variety of activities including combat, trade, and chat with other players. While the game world and the available actions therein are inspired by their offline counterparts, the games' popularity and dedicated fan base are testaments to the allure of novel social interactions granted to people by allowing them an alternative life as a new character and persona. In this paper we investigate the phenomenon of "gender swapping," which refers to players choosing avatars of genders opposite to their natural ones. We report the behavioral patterns observed in players of Fairyland Online, a globally serviced MMORPG, during social interactions when playing as in-game avatars of their own real gender or gender-swapped. We also discuss the effect of gender role and self-image in virtual social situations and the potential of our study for improving MMORPG quality and detecting online identity frauds.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {827–836},
numpages = {10},
keywords = {online social games, multiplex relations, quantitative social science, gender swapping, mmorpg},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488461,
author = {Lou, Tiancheng and Tang, Jie},
title = {Mining Structural Hole Spanners through Information Diffusion in Social Networks},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488461},
doi = {10.1145/2488388.2488461},
abstract = {The theory of structural holes suggests that individuals would benefit from filling the "holes" (called as structural hole spanners) between people or groups that are otherwise disconnected. A few empirical studies have verified that structural hole spanners play a key role in the information diffusion. However, there is still lack of a principled methodology to detect structural hole spanners from a given social network.In this work, we precisely define the problem of mining top-k structural hole spanners in large-scale social networks and provide an objective (quality) function to formalize the problem. Two instantiation models have been developed to implement the objective function. For the first model, we present an exact algorithm to solve it and prove its convergence. As for the second model, the optimization is proved to be NP-hard, and we design an efficient algorithm with provable approximation guarantees.We test the proposed models on three different networks: Coauthor, Twitter, and Inventor. Our study provides evidence for the theory of structural holes, e.g., 1% of Twitter users who span structural holes control 25% of the information diffusion on Twitter. We compare the proposed models with several alternative methods and the results show that our models clearly outperform the comparison methods. Our experiments also demonstrate that the detected structural hole spanners can help other social network applications, such as community kernel detection and link prediction. To the best of our knowledge, this is the first attempt to address the problem of mining structural hole spanners in large social networks.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {825–836},
numpages = {12},
keywords = {social network, structural hole, information diffusion, minimal cut},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488462,
author = {Ma, Richard T.B. and Lui, John C.S. and Misra, Vishal},
title = {On the Evolution of the Internet Economic Ecosystem},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488462},
doi = {10.1145/2488388.2488462},
abstract = {The evolution of the Internet has manifested itself in many ways: the traffic characteristics, the interconnection topologies and the business relationships among the autonomous components. It is important to understand why (and how) this evolution came about, and how the interplay of these dynamics may affect future evolution and services. We propose a network aware, macroscopic model that captures the characteristics and interactions of the application and network providers, and show how it leads to a market equilibrium of the ecosystem. By analyzing the driving forces and the dynamics of the market equilibrium, we obtain some fundamental understandings of the cause and effect of the Internet evolution, which explain why some historical and recent evolutions have happened. Furthermore, by projecting the likely future evolutions, our model can help application and network providers to make informed business decisions so as to succeed in this competitive ecosystem.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {849–860},
numpages = {12},
keywords = {evolution, content delivery, ip transit, internet ecosystem},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488463,
author = {Maggi, Federico and Frossi, Alessandro and Zanero, Stefano and Stringhini, Gianluca and Stone-Gross, Brett and Kruegel, Christopher and Vigna, Giovanni},
title = {Two Years of Short URLs Internet Measurement: Security Threats and Countermeasures},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488463},
doi = {10.1145/2488388.2488463},
abstract = {URL shortening services have become extremely popular. However, it is still unclear whether they are an effective and reliable tool that can be leveraged to hide malicious URLs, and to what extent these abuses can impact the end users. With these questions in mind, we first analyzed existing countermeasures adopted by popular shortening services. Surprisingly, we found such countermeasures to be ineffective and trivial to bypass. This first measurement motivated us to proceed further with a large-scale collection of the HTTP interactions that originate when web users access live pages that contain short URLs. To this end, we monitored 622 distinct URL shortening services between March 2010 and April 2012, and collected 24,953,881 distinct short URLs. With this large dataset, we studied the abuse of short URLs. Despite short URLs are a significant, new security risk, in accordance with the reports resulting from the observation of the overall phishing and spamming activity, we found that only a relatively small fraction of users ever encountered malicious short URLs. Interestingly, during the second year of measurement, we noticed an increased percentage of short URLs being abused for drive-by download campaigns and a decreased percentage of short URLs being abused for spam campaigns. In addition to these security-related findings, our unique monitoring infrastructure and large dataset allowed us to complement previous research on short URLs and analyze these web services from the user's perspective.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {861–872},
numpages = {12},
keywords = {security, crowdsourcing, short urls, measurement},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488464,
author = {Majumder, Anirban and Shrivastava, Nisheeth},
title = {Know Your Personalization: Learning Topic Level Personalization in Online Services},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488464},
doi = {10.1145/2488388.2488464},
abstract = {Online service platforms (OSPs), such as search engines, news-websites, ad-providers, etc., serve highly personalized content to the user, based on the profile extracted from her history with the OSP. In this paper, we capture OSP's personalization for an user in a new data structure called the personalization vector (?), which is a weighted vector over a set of topics, and present efficient algorithms to learn it.Our approach treats OSPs as black-boxes, and extracts η by mining only their output, specifically, the personalized (for an user) and vanilla (without any user information) contents served, and the differences in these content. We believe that such treatment of OSPs is a unique aspect of our work, not just enabling access to (so far hidden) profiles in OSPs, but also providing a novel and practical approach for retrieving information from OSPs by mining differences in their outputs.We formulate a new model called Latent Topic Personalization (LTP) that captures the personalization vector in a learning framework and present efficient inference algorithms for determining it. We perform extensive experiments targeting search engine personalization, using data from both real Google users and synthetic setup. Our results indicate that LTP achieves high accuracy (R-pre = 84%) in discovering personalized topics.For Google data, our qualitative results demonstrate that the topics determined by LTP for a user correspond well to his ad-categories determined by Google.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {873–884},
numpages = {12},
keywords = {online service providers, topics, learning, search personalization},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488465,
author = {Marshall, Catherine C. and Shipman, Frank M.},
title = {Saving, Reusing, and Remixing Web Video: Using Attitudes and Practices to Reveal Social Norms},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488465},
doi = {10.1145/2488388.2488465},
abstract = {The growth of online videos has spurred a concomitant increase in the storage, reuse, and remix of this content. As we gain more experience with video content, social norms about ownership have evolved accordingly, spelling out what people think is appropriate use of content that is not necessarily their own. We use a series of three studies, each centering on a different genre of recordings, to probe 634 participants' attitudes toward video storage, reuse, and remix; we also question participants about their own experiences with online video. The results allow us to characterize current practice and emerging social norms and to establish the relationship between the two. Hypotheticals borrowed from legal research are used as the primary vehicle for testing attitudes, and for identifying boundaries between socially acceptable and unacceptable behavior.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {885–896},
numpages = {12},
keywords = {social media, property rights, social norms, video reuse},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488466,
author = {McAuley, Julian John and Leskovec, Jure},
title = {From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488466},
doi = {10.1145/2488388.2488466},
abstract = {Recommending products to consumers means not only understanding their tastes, but also understanding their level of experience. For example, it would be a mistake to recommend the iconic film Seven Samurai simply because a user enjoys other action movies; rather, we might conclude that they will eventually enjoy it---once they are ready. The same is true for beers, wines, gourmet foods---or any products where users have acquired tastes: the `best' products may not be the most 'accessible'. Thus our goal in this paper is to recommend products that a user will enjoy now, while acknowledging that their tastes may have changed over time, and may change again in the future. We model how tastes change due to the very act of consuming more products---in other words, as users become more experienced. We develop a latent factor recommendation system that explicitly accounts for each user's level of experience. We find that such a model not only leads to better recommendations, but also allows us to study the role of user experience and expertise on a novel dataset of fifteen million beer, wine, food, and movie reviews.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {897–908},
numpages = {12},
keywords = {expertise, recommender systems, user modeling},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488467,
author = {Moghaddam, Samaneh and Ester, Martin},
title = {The FLDA Model for Aspect-Based Opinion Mining: Addressing the Cold Start Problem},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488467},
doi = {10.1145/2488388.2488467},
abstract = {Aspect-based opinion mining from online reviews has attracted a lot of attention recently. The main goal of all of the proposed methods is extracting aspects and/or estimating aspect ratings. Recent works, which are often based on Latent Dirichlet Allocation (LDA), consider both tasks simultaneously. These models are normally trained at the item level, i.e., a model is learned for each item separately. Learning a model per item is fine when the item has been reviewed extensively and has enough training data. However, in real-life data sets such as those from Epinions.com and Amazon.com more than 90% of items have less than 10 reviews, so-called cold start items. State-of-the-art LDA models for aspect-based opinion mining are trained at the item level and therefore perform poorly for cold start items due to the lack of sufficient training data. In this paper, we propose a probabilistic graphical model based on LDA, called Factorized LDA (FLDA), to address the cold start problem. The underlying assumption of FLDA is that aspects and ratings of a review are influenced not only by the item but also by the reviewer. It further assumes that both items and reviewers can be modeled by a set of latent factors which represent their aspect and rating distributions. Different from state-of-the-art LDA models, FLDA is trained at the category level and learns the latent factors using the reviews of all the items of a category, in particular the non cold start items, and uses them as prior for cold start items. Our experiments on three real-life data sets demonstrate the improved effectiveness of the FLDA model in terms of likelihood of the held-out test set. We also evaluate the accuracy of FLDA based on two application-oriented measures.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {909–918},
numpages = {10},
keywords = {aspect identification, rating prediction, latent dirichlet allocation, user modeling, cold start item, aspect-based opinion mining},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488468,
author = {Molavi Kakhki, Arash and Kliman-Silver, Chloe and Mislove, Alan},
title = {Iolaus: Securing Online Content Rating Systems},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488468},
doi = {10.1145/2488388.2488468},
abstract = {Online content ratings services allow users to find and share content ranging from news articles (Digg) to videos (YouTube) to businesses (Yelp). Generally, these sites allow users to create accounts, declare friendships, upload and rate content, and locate new content by leveraging the aggregated ratings of others. These services are becoming increasingly popular; Yelp alone has over 33 million reviews. Unfortunately, this popularity is leading to increasing levels of malicious activity, including multiple identity (Sybil) attacks and the "buying" of ratings from users.In this paper, we present Iolaus, a system that leverages the underlying social network of online content rating systems to defend against such attacks. Iolaus uses two novel techniques: (a) weighing ratings to defend against multiple identity attacks and (b) relative ratings to mitigate the effect of "bought" ratings. An evaluation of Iolaus using microbenchmarks, synthetic data, and real-world content rating data demonstrates that Iolaus is able to outperform existing approaches and serve as a practical defense against multiple-identity and rating-buying attacks.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {919–930},
numpages = {12},
keywords = {social network, vote buying, content rating, sybil},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488469,
author = {Moshfeghi, Yashar and Jose, Joemon M.},
title = {On Cognition, Emotion, and Interaction Aspects of Search Tasks with Different Search Intentions},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488469},
doi = {10.1145/2488388.2488469},
abstract = {The complex and dynamic nature of search processes surrounding information seeking have been exhaustively studied. Recent studies have highlighted search processes with different intentions, such as those for entertainment purposes or re-finding a visited information object, are fundamentally different in nature to typical information seeking intentions. Despite the popularity of such search processes on the Web, they have not yet been thoroughly explored. Using a video retrieval system as a use case, we study the characteristics of four different search task types: seeking information, re-finding a particular information object, and two different entertainment intentions (i.e. entertainment by adjusting arousal level, and entertainment by adjusting mood). In particular, we looked at the cognition, emotion and action aspects of these search tasks at different phases of a search process. This follows the common assumption in the information seeking and retrieval community that a complex search process can be broken down into a relatively small number of activity phases. Our experimental results show significant differences in the characteristics of studied search tasks. Furthermore, we investigate whether we can predict these search tasks given user's interaction with the system. Results show that we can learn a model that predicts the search task types with reasonable accuracy. Overall, these findings may help to steer search engines to better satisfy searchers' needs beyond typically assumed information seeking processes.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {931–942},
numpages = {12},
keywords = {re-finding, search intents, cognition, entertainment, prediction, emotion, interaction},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488470,
author = {Nath, Abhirup and Mukherjee, Shibnath and Jain, Prateek and Goyal, Navin and Laxman, Srivatsan},
title = {Ad Impression Forecasting for Sponsored Search},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488470},
doi = {10.1145/2488388.2488470},
abstract = {A typical problem for a search engine (hosting sponsored search service) is to provide the advertisers with a forecast of the number of impressions his/her ad is likely to obtain for a given bid. Accurate forecasts have high business value, since they enable advertisers to select bids that lead to better returns on their investment. They also play an important role in services such as automatic campaign optimization. Despite its importance the problem has remained relatively unexplored in literature. Existing methods typically overfit to the training data, leading to inconsistent performance. Furthermore, some of the existing methods cannot provide predictions for new ads, i.e., for ads that are not present in the logs. In this paper, we develop a generative model based approach that addresses these drawbacks. We design a Bayes net to capture inter-dependencies between the query traffic features and the competitors in an auction. Furthermore, we account for variability in the volume of query traffic by using a dynamic linear model. Finally, we implement our approach on a production grade MapReduce framework and conduct extensive large scale experiments on substantial volumes of sponsored search data from Bing. Our experimental results demonstrate significant advantages over existing methods as measured using several accuracy/error criteria, improved ability to provide estimates for new ads and more consistent performance with smaller variance in accuracies. Our method can also be adapted to several other related forecasting problems such as predicting average position of ads or the number of clicks under budget constraints.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {943–952},
numpages = {10},
keywords = {bayes net, sponsored search, dynamic linear model, auctions},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488471,
author = {Navalpakkam, Vidhya and Jentzsch, LaDawn and Sayres, Rory and Ravi, Sujith and Ahmed, Amr and Smola, Alex},
title = {Measurement and Modeling of Eye-Mouse Behavior in the Presence of Nonlinear Page Layouts},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488471},
doi = {10.1145/2488388.2488471},
abstract = {As search pages are becoming increasingly complex, with images and nonlinear page layouts, understanding how users examine the page is important. We present a lab study on the effect of a rich informational panel to the right of the search result column, on eye and mouse behavior. Using eye and mouse data, we show that the flow of user attention on nonlinear page layouts is different from the widely believed top-down linear examination order of search results. We further demonstrate that the mouse, like the eye, is sensitive to two key attributes of page elements -- their position (layout), and their relevance to the user's task. We identify mouse measures that are strongly correlated with eye movements, and develop models to predict user attention (eye gaze) from mouse activity. These findings show that mouse tracking can be used to infer user attention and information flow patterns on search pages. Potential applications include ranking, search page optimization, and UI evaluation.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {953–964},
numpages = {12},
keywords = {mouse, measurement, attention, prediction, web search, eye},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488472,
author = {Nencioni, Gianfranco and Sastry, Nishanth and Chandaria, Jigna and Crowcroft, Jon},
title = {Understanding and Decreasing the Network Footprint of Catch-up Tv},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488472},
doi = {10.1145/2488388.2488472},
abstract = {"Catch-up", or on-demand access of previously broadcast TV content over the public Internet, constitutes a significant fraction of peak time network traffic. This paper analyses consumption patterns of nearly 6 million users of a nationwide deployment of a catch-up TV service, to understand the network support required. We find that catch-up has certain natural scaling properties compared to traditional TV: The on-demand nature spreads load over time, and users have much higher completion rates for content streams than previously reported. Users exhibit strong preferences for serialised content, and for specific genres.Exploiting this, we design a Speculative Content Offloading and Recording Engine (SCORE) that predictively records a personalised set of shows on user-local storage, and thereby offloads traffic that might result from subsequent catch-up access. Evaluations show that even with a modest storage of ~32GB, an oracle with complete knowledge of user consumption can save up to 74% of the energy, and 97% of the peak bandwidth compared to the current IP streaming-based architecture. In the best case, optimising for energy consumption, SCORE can recover more than 60% of the traffic and energy savings achieved by the oracle. Optimising purely for traffic rather than energy can reduce bandwith by an additional 5%.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {965–976},
numpages = {12},
keywords = {traffic offloading, energy savings, workload characterization, catch-up tv, ott content, network footprint},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488473,
author = {Ngonga Ngomo, Axel-Cyrille and B\"{u}hmann, Lorenz and Unger, Christina and Lehmann, Jens and Gerber, Daniel},
title = {Sorry, i Don't Speak SPARQL: Translating SPARQL Queries into Natural Language},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488473},
doi = {10.1145/2488388.2488473},
abstract = {Over the past years, Semantic Web and Linked Data technologies have reached the backend of a considerable number of applications. Consequently, large amounts of RDF data are constantly being made available across the planet. While experts can easily gather information from this wealth of data by using the W3C standard query language SPARQL, most lay users lack the expertise necessary to proficiently interact with these applications. Consequently, non-expert users usually have to rely on forms, query builders, question answering or keyword search tools to access RDF data. However, these tools have so far been unable to explicate the queries they generate to lay users, making it difficult for these users to i) assess the correctness of the query generated out of their input, and ii) to adapt their queries or iii) to choose in an informed manner between possible interpretations of their input. This paper addresses this drawback by presenting SPARQL2NL, a generic approach that allows verbalizing SPARQL queries, i.e., converting them into natural language. Our framework can be integrated into applications where lay users are required to understand SPARQL or to generate SPARQL queries in a direct (forms, query builders) or an indirect (keyword search, question answering) manner. We evaluate our approach on the DBpedia question set provided by QALD-2 within a survey setting with both SPARQL experts and lay users. The results of the 115 filled surveys show that SPARQL2NL can generate complete and easily understandable natural language descriptions. In addition, our results suggest that even SPARQL experts can process the natural language representation of SPARQL queries computed by our approach more efficiently than the corresponding SPARQL queries. Moreover, non-experts are enabled to reliably understand the content of SPARQL queries.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {977–988},
numpages = {12},
keywords = {sparql, query verbalization, natural language generation},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488474,
author = {Nikiforakis, Nick and Van Acker, Steven and Meert, Wannes and Desmet, Lieven and Piessens, Frank and Joosen, Wouter},
title = {Bitsquatting: Exploiting Bit-Flips for Fun, or Profit?},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488474},
doi = {10.1145/2488388.2488474},
abstract = {Over the last fifteen years, several types of attacks against domain names and the companies relying on them have been observed. The well-known cybersquatting of domain names gave way to typosquatting, the abuse of a user's mistakes when typing a URL in her browser's address bar. Recently, a new attack against domain names surfaced, namely bitsquatting. In bitsquatting, an attacker leverages random bit-errors occurring in the memory of commodity computers and smartphones, to redirect Internet traffic to attacker-controlled domains.In this paper, we report on a large-scale experiment, measuring the adoption of bitsquatting by the domain-squatting community through the tracking of registrations of bitsquatting domains targeting popular web sites over a 9-month period. We show how new bitsquatting domains are registered daily and how attackers are trying to monetize their domains through the use of ads, abuse of affiliate programs and even malware installations. Lastly, given the discovered prevalence of bitsquatting, we review possible defense measures that companies, software developers and Internet Service Providers can use to protect against it.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {989–998},
numpages = {10},
keywords = {affiliate abuse, domain name, cybersquatting, bitsquatting},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488475,
author = {Paquet, Ulrich and Koenigstein, Noam},
title = {One-Class Collaborative Filtering with Random Graphs},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488475},
doi = {10.1145/2488388.2488475},
abstract = {The bane of one-class collaborative filtering is interpreting and modelling the latent signal from the missing class. In this paper we present a novel Bayesian generative model for implicit collaborative filtering. It forms a core component of the Xbox Live architecture, and unlike previous approaches, delineates the odds of a user disliking an item from simply being unaware of it. The latent signal is treated as an unobserved random graph connecting users with items they might have encountered. We demonstrate how large-scale distributed learning can be achieved through a combination of stochastic gradient descent and mean field variational inference over random graph samples. A fine-grained comparison is done against a state of the art baseline on real world data.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {999–1008},
numpages = {10},
keywords = {random graph, variational inference, one-class collaborative filtering},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488476,
author = {Pasternack, Jeff and Roth, Dan},
title = {Latent Credibility Analysis},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488476},
doi = {10.1145/2488388.2488476},
abstract = {A frequent problem when dealing with data gathered from multiple sources on the web (ranging from booksellers to Wikipedia pages to stock analyst predictions) is that these sources disagree, and we must decide which of their (often mutually exclusive) claims we should accept. Current state-of-the-art information credibility algorithms known as "fact-finders" are transitive voting systems with rules specifying how votes iteratively flow from sources to claims and then back to sources. While this is quite tractable and often effective, fact-finders also suffer from substantial limitations; in particular, a lack of transparency obfuscates their credibility decisions and makes them difficult to adapt and analyze: knowing the mechanics of how votes are calculated does not readily tell us what those votes mean, and finding, for example, that a source has a score of 6 is not informative. We introduce a new approach to information credibility, Latent Credibility Analysis (LCA), constructing strongly principled, probabilistic models where the truth of each claim is a latent variable and the credibility of a source is captured by a set of model parameters. This gives LCA models clear semantics and modularity that make extending them to capture additional observed and latent credibility factors straightforward. Experiments over four real-world datasets demonstrate that LCA models can outperform the best fact-finders in both unsupervised and semi-supervised settings.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1009–1020},
numpages = {12},
keywords = {trust, credibility, veracity, graphical models},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488477,
author = {Patil, Akshay and Liu, Juan and Gao, Jie},
title = {Predicting Group Stability in Online Social Networks},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488477},
doi = {10.1145/2488388.2488477},
abstract = {Social groups often exhibit a high degree of dynamism. Some groups thrive, while many others die over time. Modeling group stability dynamics and understanding whether/when a group will remain stable or shrink over time can be important in a number of social domains. In this paper, we study two different types of social networks as exemplar platforms for modeling and predicting group stability dynamics. We build models to predict if a group is going to remain stable or is likely to shrink over a period of time. We observe that both the level of member diversity and social activities are critical in maintaining the stability of groups. We also find that certain 'prolific' members play a more important role in maintaining the group stability. Our study shows that group stability can be predicted with high accuracy, and feature diversity is critical to prediction performance.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1021–1030},
numpages = {10},
keywords = {online communities, social networks, group stability},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488478,
author = {Puzis, Yury and Borodin, Yevgen and Puzis, Rami and Ramakrishnan, I.V.},
title = {Predictive Web Automation Assistant for People with Vision Impairments},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488478},
doi = {10.1145/2488388.2488478},
abstract = {The Web is far less usable and accessible for people with vision impairments than it is for sighted people. Web automation, a process of automating browsing actions on behalf of the user, has the potential to bridge the divide between the ways sighted and people with vision impairment access the Web; specifically, it can enable the latter to breeze through web browsing tasks that beforehand were slow, hard, or even impossible to accomplish. Typical web automation requires that the user record a macro, a sequence of browsing steps, so that these steps can be automated in the future by replaying the macro. However, for people with vision impairment, automation with macros is not usable.In this paper, we propose a novel model-based approach that facilitates web automation without having to either record or replay macros. Using the past browsing history and the current web page as the browsing context, the proposed model can predict the most probable browsing actions that the user can do. The model construction is "unsupervised". More importantly, the model is continuously and incrementally updated as history evolves, thereby, ensuring the predictions are not "outdated".We also describe a novel interface that lets the user focus on the objects associated with the most probable predicted browsing steps (e.g., clicking links and filling out forms), and facilitates automatic execution of the selected steps. A study with 19 blind participants showed that the proposed approach dramatically reduced the interaction time needed to accomplish typical browsing tasks, and the user interface was perceived to be much more usable than the standard screen-reading interfaces.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1031–1040},
numpages = {10},
keywords = {adaptive interface, browser, accessibility, model, low vision, macro, interface agent, prediction, screen-reader, non-visual, sequence alignment, web, automation, blind},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488479,
author = {Qi, Guo-Jun and Aggarwal, Charu C. and Han, Jiawei and Huang, Thomas},
title = {Mining Collective Intelligence in Diverse Groups},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488479},
doi = {10.1145/2488388.2488479},
abstract = {Collective intelligence, which aggregates the shared information from large crowds, is often negatively impacted by unreliable information sources with the low quality data. This becomes a barrier to the effective use of collective intelligence in a variety of applications. In order to address this issue, we propose a probabilistic model to jointly assess the reliability of sources and find the true data. We observe that different sources are often not independent of each other. Instead, sources are prone to be mutually influenced, which makes them dependent when sharing information with each other. High dependency between sources makes collective intelligence vulnerable to the overuse of redundant (and possibly incorrect) information from the dependent sources. Thus, we reveal the latent group structure among dependent sources, and aggregate the information at the group level rather than from individual sources directly. This can prevent the collective intelligence from being inappropriately dominated by dependent sources. We will also explicitly reveal the reliability of groups, and minimize the negative impacts of unreliable groups. Experimental results on real-world data sets show the effectiveness of the proposed approach with respect to existing algorithms.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1041–1052},
numpages = {12},
keywords = {robust classifier, collective intelligence, crowdsourcing},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488480,
author = {Qu, Yan and Zhang, Jun},
title = {Trade Area Analysis Using User Generated Mobile Location Data},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488480},
doi = {10.1145/2488388.2488480},
abstract = {In this paper, we illustrate how User Generated Mobile Location Data (UGMLD) like Foursquare check-ins can be used in Trade Area Analysis (TAA) by introducing a new framework and corresponding analytic methods. Three key processes were created: identifying the activity center of a mobile user, profiling users based on their location history, and modeling users' preference probability. Extensions to traditional TAA are introduced, including customer-centric distance decay analysis and check-in sequence analysis. Adopting the rich content and context of UGMLD, these methods introduce new dimensions to modeling and delineating trade areas. Analyzing customers' visits to a business in the context of their daily life sheds new light on the nature and performance of the venue. This work has important business implications in the field of mobile computing.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1053–1064},
numpages = {12},
keywords = {trade area analysis, location based marketing, mobile marketing},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488481,
author = {Quercia, Daniele and Pesce, Joao Paulo and Almeida, Virgilio and Crowcroft, Jon},
title = {Psychological Maps 2.0: A Web Engagement Enterprise Starting in London},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488481},
doi = {10.1145/2488388.2488481},
abstract = {Planners and social psychologists have suggested that the recognizability of the urban environment is linked to people's socio-economic well-being. We build a web game that puts the recognizability of London's streets to the test. It follows as closely as possible one experiment done by Stanley Milgram in 1972. The game picks up random locations from Google Street View and tests users to see if they can judge the location in terms of closest subway station, borough, or region. Each participant dedicates only few minutes to the task (as opposed to 90 minutes in Milgram's). We collect data from 2,255 participants (one order of magnitude a larger sample) and build a recognizability map of London based on their responses. We find that some boroughs have little cognitive representation; that recognizability of an area is explained partly by its exposure to Flickr and Foursquare users and mostly by its exposure to subway passengers; and that areas with low recognizability do not fare any worse on the economic indicators of income, education, and employment, but they do significantly suffer from social problems of housing deprivation, poor living conditions, and crime. These results could not have been produced without analyzing life off- and online: that is, without considering the interactions between urban places in the physical world and their virtual presence on platforms such as Flickr and Foursquare. This line of work is at the crossroad of two emerging themes in computing research - a crossroad where "web science" meets the "smart city" agenda.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1065–1076},
numpages = {12},
keywords = {web science, social media, engagement, urban informatics},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488482,
author = {Rangapuram, Syama Sundar and B\"{u}hler, Thomas and Hein, Matthias},
title = {Towards Realistic Team Formation in Social Networks Based on Densest Subgraphs},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488482},
doi = {10.1145/2488388.2488482},
abstract = {Given a task T, a set of experts V with multiple skills and a social network G(V, W) reflecting the compatibility among the experts, team formation is the problem of identifying a team C ? V that is both competent in performing the task T and compatible in working together. Existing methods for this problem make too restrictive assumptions and thus cannot model practical scenarios. The goal of this paper is to consider the team formation problem in a realistic setting and present a novel formulation based on densest subgraphs. Our formulation allows modeling of many natural requirements such as (i) inclusion of a designated team leader and/or a group of given experts, (ii) restriction of the size or more generally cost of the team (iii) enforcing locality of the team, e.g., in a geographical sense or social sense, etc. The proposed formulation leads to a generalized version of the classical densest subgraph problem with cardinality constraints (DSP), which is an NP hard problem and has many applications in social network analysis. In this paper, we present a new method for (approximately) solving the generalized DSP (GDSP). Our method, FORTE, is based on solving an equivalent continuous relaxation of GDSP. The solution found by our method has a quality guarantee and always satisfies the constraints of GDSP. Experiments show that the proposed formulation (GDSP) is useful in modeling a broader range of team formation problems and that our method produces more coherent and compact teams of high quality. We also show, with the help of an LP relaxation of GDSP, that our method gives close to optimal solutions to GDSP.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1077–1088},
numpages = {12},
keywords = {social networks, team formation, densest subgraphs},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488483,
author = {Ruan, Yiye and Fuhry, David and Parthasarathy, Srinivasan},
title = {Efficient Community Detection in Large Networks Using Content and Links},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488483},
doi = {10.1145/2488388.2488483},
abstract = {In this paper we discuss a very simple approach of combining content and link information in graph structures for the purpose of community discovery, a fundamental task in network analysis. Our approach hinges on the basic intuition that many networks contain noise in the link structure and that content information can help strengthen the community signal. This enables ones to eliminate the impact of noise (false positives and false negatives), which is particularly prevalent in online social networks and Web-scale information networks.Specifically we introduce a measure of signal strength between two nodes in the network by fusing their link strength with content similarity. Link strength is estimated based on whether the link is likely (with high probability) to reside within a community. Content similarity is estimated through cosine similarity or Jaccard coefficient.We discuss a simple mechanism for fusing content and link similarity. We then present a biased edge sampling procedure which retains edges that are locally relevant for each graph node. The resulting backbone graph can be clustered using standard community discovery algorithms such as Metis and Markov clustering.Through extensive experiments on multiple real-world datasets (Flickr, Wikipedia and CiteSeer) with varying sizes and characteristics, we demonstrate the effectiveness and efficiency of our methods over state-of-the-art learning and mining approaches several of which also attempt to combine link and content analysis for the purposes of community discovery. Specifically we always find a qualitative benefit when combining content with link analysis. Additionally our biased graph sampling approach realizes a quantitative benefit in that it is typically several orders of magnitude faster than competing approaches.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1089–1098},
numpages = {10},
keywords = {graph clustering, content analysis, web mining},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488484,
author = {Sawant, Uma and Chakrabarti, Soumen},
title = {Learning Joint Query Interpretation and Response Ranking},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488484},
doi = {10.1145/2488388.2488484},
abstract = {Thanks to information extraction and semantic Web efforts, search on unstructured text is increasingly refined using semantic annotations and structured knowledge bases. However, most users cannot become familiar with the schema of knowledge bases and ask structured queries. Interpreting free-format queries into a more structured representation is of much current interest. The dominant paradigm is to segment or partition query tokens by purpose (references to types, entities, attribute names, attribute values, relations) and then launch the interpreted query on structured knowledge bases. Given that structured knowledge extraction is never complete, here we choose a less trodden path: a data representation that retains the unstructured text corpus, along with structured annotations (mentions of entities and relationships) on it. We propose two new, natural formulations for joint query interpretation and response ranking that exploit bidirectional flow of information between the knowledge base and the corpus. One, inspired by probabilistic language models, computes expected response scores over the uncertainties of query interpretation. The other is based on max-margin discriminative learning, with latent variables representing those uncertainties. In the context of typed entity search, both formulations bridge a considerable part of the accuracy gap between a generic query that does not constrain the type at all, and the upper bound where the "perfect" target entity type of each query is provided by humans. Our formulations are also superior to a two-stage approach of first choosing a target type using recent query type prediction techniques, and then launching a type-restricted entity search query.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1099–1110},
numpages = {12},
keywords = {query interpretation, entity search},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488485,
author = {Schien, Daniel and Shabajee, Paul and Wood, Stephen G. and Preist, Chris},
title = {A Model for Green Design of Online News Media Services},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488485},
doi = {10.1145/2488388.2488485},
abstract = {The use of information and communication technology and the web-based products it provides is responsible for significant emissions of greenhouse gases. In order to enable the reduction of emissions during the design of such products, it is necessary to estimate as accurately as possible their carbon impact over the entire product system. In this work we describe a new method which combines models of energy consumption during the use of digital media with models of the behavior of the audience. We apply this method to conduct an assessment of the annual carbon emissions for the product suite of a major international news organization. We then demonstrate its use for green design by evaluating the impacts of five different interventions on the product suite. We find that carbon footprint of the online newspaper amounts to approximately 7700 tCO2e per year, of which 75% are caused by the user devices. Among the evaluated scenarios a significant uptake of eReaders in favor of PCs has the greatest reduction potential. Our results also show that even a significant reduction of data volume on a web page would only result in small overall energy savings.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1111–1122},
numpages = {12},
keywords = {sustainability, green software engineering, digital media, carbon footprinting},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488486,
author = {Schoenebeck, Grant},
title = {Potential Networks, Contagious Communities, and Understanding Social Network Structure},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488486},
doi = {10.1145/2488388.2488486},
abstract = {In this paper we study how the network of agents adopting a particular technology relates to the structure of the underlying network over which the technology adoption spreads. We develop a model and show that the network of agents adopting a particular technology may have characteristics that differ significantly from the social network of agents over which the technology spreads. For example, the network induced by a cascade may have a heavy-tailed degree distribution even if the original network does not.This provides evidence that online social networks created by technology adoption over an underlying social network may look fundamentally different from social networks and indicates that using data from many online social networks may mislead us if we try to use it to directly infer the structure of social networks. Our results provide an alternate explanation for certain properties repeatedly observed in data sets, for example: heavy-tailed degree distribution, network densification, shrinking diameter, and network community profile. These properties could be caused by a sort of sampling bias rather than by attributes of the underlying social structure. By generating networks using cascades over traditional network models that do not themselves contain these properties, we can nevertheless reliably produce networks that contain all these properties.An opportunity for interesting future research is developing new methods that correctly infer underlying network structure from data about a network that is generated via a cascade spread over the underlying network.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1123–1132},
numpages = {10},
keywords = {random graphs, social networks, community structure},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488487,
author = {Sharma, Amit and Cosley, Dan},
title = {Do Social Explanations Work? Studying and Modeling the Effects of Social Explanations in Recommender Systems},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488487},
doi = {10.1145/2488388.2488487},
abstract = {Recommender systems associated with social networks often use social explanations (e.g. "X, Y and 2 friends like this") to support the recommendations. We present a study of the effects of these social explanations in a music recommendation context. We start with an experiment with 237 users, in which we show explanations with varying levels of social information and analyze their effect on users' decisions. We distinguish between two key decisions: the likelihood of checking out the recommended artist, and the actual rating of the artist based on listening to several songs. We find that while the explanations do have some influence on the likelihood, there is little correlation between the likelihood and actual (listening) rating for the same artist. Based on these insights, we present a generative probabilistic model that explains the interplay between explanations and background information on music preferences, and how that leads to a final likelihood rating for an artist. Acknowledging the impact of explanations, we discuss a general recommendation framework that models external informational elements in the recommendation interface, in addition to inherent preferences of users.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1133–1144},
numpages = {12},
keywords = {recommender systems, social explanation, influence},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488488,
author = {Shekarpour, Saeedeh and Ngonga Ngomo, Axel-Cyrille and Auer, S\"{o}ren},
title = {Question Answering on Interlinked Data},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488488},
doi = {10.1145/2488388.2488488},
abstract = {The Data Web contains a wealth of knowledge on a large number of domains. Question answering over interlinked data sources is challenging due to two inherent characteristics. First, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain question. Second, constructing a federated formal query across different datasets requires exploiting links between the different datasets on both the schema and instance levels. We present a question answering system, which transforms user supplied queries (i.e. natural language sentences or keywords) into conjunctive SPARQL queries over a set of interlinked data sources. The contribution of this paper is two-fold: Firstly, we introduce a novel approach for determining the most suitable resources for a user-supplied query from different datasets (disambiguation). We employ a hidden Markov model, whose parameters were bootstrapped with different distribution functions. Secondly, we present a novel method for constructing a federated formal queries using the disambiguated resources and leveraging the linking structure of the underlying datasets. This approach essentially relies on a combination of domain and range inference as well as a link traversal method for constructing a connected graph which ultimately renders a corresponding SPARQL query. The results of our evaluation with three life-science datasets and 25 benchmark queries demonstrate the effectiveness of our approach.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1145–1156},
numpages = {12},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488489,
author = {Singer, Yaron and Mittal, Manas},
title = {Pricing Mechanisms for Crowdsourcing Markets},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488489},
doi = {10.1145/2488388.2488489},
abstract = {Every day millions of crowdsourcing tasks are performed in exchange for payments. Despite the important role pricing plays in crowdsourcing campaigns and the complexity of the market, most platforms do not provide requesters appropriate tools for effective pricing and allocation of tasks.In this paper, we introduce a framework for designing mechanisms with provable guarantees in crowdsourcing markets. The framework enables automating the process of pricing and allocation of tasks for requesters in complex markets like Amazon's Mechanical Turk where workers arrive in an online fashion and requesters face budget constraints and task completion deadlines. We present constant-competitive incentive compatible mechanisms for maximizing the number of tasks under a budget, and for minimizing payments given a fixed number of tasks to complete. To demonstrate the effectiveness of this framework we created a platform that enables applying pricing mechanisms in markets like Mechanical Turk. The platform allows us to show that the mechanisms we present here work well in practice, as well as to give experimental evidence to workers' strategic behavior in absence of appropriate incentive schemes.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1157–1166},
numpages = {10},
keywords = {mechanical turk, human computation, mechanism design, crowdsourcing, mechanical perk},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488490,
author = {Singla, Adish and Krause, Andreas},
title = {Truthful Incentives in Crowdsourcing Tasks Using Regret Minimization Mechanisms},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488490},
doi = {10.1145/2488388.2488490},
abstract = {What price should be offered to a worker for a task in an online labor market? How can one enable workers to express the amount they desire to receive for the task completion? Designing optimal pricing policies and determining the right monetary incentives is central to maximizing requester's utility and workers' profits. Yet, current crowdsourcing platforms only offer a limited capability to the requester in designing the pricing policies and often rules of thumb are used to price tasks. This limitation could result in inefficient use of the requester's budget or workers becoming disinterested in the task.In this paper, we address these questions and present mechanisms using the approach of regret minimization in online learning. We exploit a link between procurement auctions and multi-armed bandits to design mechanisms that are budget feasible, achieve near-optimal utility for the requester, are incentive compatible (truthful) for workers and make minimal assumptions about the distribution of workers' true costs. Our main contribution is a novel, no-regret posted price mechanism, BP-UCB, for budgeted procurement in stochastic online settings. We prove strong theoretical guarantees about our mechanism, and extensively evaluate it in simulations as well as on real data from the Mechanical Turk platform. Compared to the state of the art, our approach leads to a 180% increase in utility.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1167–1178},
numpages = {12},
keywords = {regret minimization, multi-armed bandits, crowdsourcing, procurement auctions, incentive compatible mechanisms, posted prices},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488491,
author = {Sodomka, Eric and Lahaie, S\'{e}bastien and Hillard, Dustin},
title = {A Predictive Model for Advertiser Value-per-Click in Sponsored Search},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488491},
doi = {10.1145/2488388.2488491},
abstract = {Sponsored search is a form of online advertising where advertisers bid for placement next to search engine results for specific keywords. As search engines compete for the growing share of online ad spend, it becomes important for them to understand what keywords advertisers value most, and what characteristics of keywords drive value. In this paper we propose an approach to keyword value prediction that draws on advertiser bidding behavior across the terms and campaigns in an account. We provide original insights into the structure of sponsored search accounts that motivate the use of a hierarchical modeling strategy. We propose an economically meaningful loss function which allows us to implicitly fit a linear model for values given observables such as bids and click-through rates. The model draws on demographic and textual features of keywords and takes advantage of the hierarchical structure of sponsored search accounts. Its predictive quality is evaluated on several high-revenue and high-exposure advertising accounts on a major search engine. Besides the general evaluation of advertiser welfare, our approach has potential applications to keyword and bid suggestion.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1179–1190},
numpages = {12},
keywords = {hierarchical model, regret loss function, sponsored search},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488492,
author = {Song, Jonghyuk and Lee, Sangho and Kim, Jong},
title = {I Know the Shortened URLs You Clicked on Twitter: Inference Attack Using Public Click Analytics and Twitter Metadata},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488492},
doi = {10.1145/2488388.2488492},
abstract = {Twitter is a popular social network service for sharing messages among friends. Because Twitter restricts the length of messages, many Twitter users use URL shortening services, such as bit.ly and goo.gl, to share long URLs with friends. Some URL shortening services also provide click analytics of the shortened URLs, including the number of clicks, countries, platforms, browsers and referrers. To protect visitors' privacy, they do not reveal identifying information about individual visitors. In this paper, we propose a practical attack technique that can infer who clicks what shortened URLs on Twitter. Unlike the conventional browser history stealing attacks, our attack methods only need publicly available information provided by URL shortening services and Twitter. Evaluation results show that our attack technique can compromise Twitter users' privacy with high accuracy.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1191–1200},
numpages = {10},
keywords = {twitter, privacy leak, url shortening service, inference},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488493,
author = {Song, Yang and Ma, Hao and Wang, Hongning and Wang, Kuansan},
title = {Exploring and Exploiting User Search Behavior on Mobile and Tablet Devices to Improve Search Relevance},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488493},
doi = {10.1145/2488388.2488493},
abstract = {In this paper, we present a log-based study on user search behavior comparisons on three different platforms: desktop, mobile and tablet. We use three-month search logs in 2012 from a commercial search engine for our study. Our objective is to better understand how and to what extent mobile and tablet searchers behave differently than desktop users. Our study spans a variety of aspects including query categorization, query length, search time distribution, search location distribution, user click patterns and so on. From our data set, we reveal that there are significant differences between user search patterns in these three platforms, and therefore use the same ranking system is not an optimal solution for all of them. Consequently, we propose a framework that leverages a set of domain-specific features, along with the training data from desktop search, to further improve the search relevance for mobile and tablet platforms. Experimental results demonstrate that by transferring knowledge from desktop search, search relevance on mobile and tablet can be greatly improved.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1201–1212},
numpages = {12},
keywords = {tablet search, user behavior analysis, mobile search, search result ranking},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488494,
author = {Song, Yang and Shi, Xiaolin and Fu, Xin},
title = {Evaluating and Predicting User Engagement Change with Degraded Search Relevance},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488494},
doi = {10.1145/2488388.2488494},
abstract = {User engagement in search refers to the frequency for users (re-)using the search engine to accomplish their tasks. Among factors that affected users' visit frequency, relevance of search results is believed to play a pivotal role. While multiple work in the past has demonstrated the correlation between search success and user engagement based on longitudinal analysis, we examine this problem from a different perspective in this work. Specifically, we carefully designed a large-scale controlled experiment on users of a large commercial Web search engine, in which users were separated into control and treatment groups, where users in treatment group were presented with search results which are deliberate degraded in relevance. We studied users' responses to the relevance degradation through tracking several behavioral metrics (such as query per user, click per session) over an extended period of time both during and following the experiment. By quantifying the relationship between user engagement and search relevance, we observe significant differences between user's short-term search behavior and long-term engagement change. By leveraging some of the key findings from the experiment, we developed a machine learning model to predict the long term impact of relevance degradation on user engagement. Overall, our model achieves over 67% of accuracy in predicting user engagement drop. Besides, our model is also capable of predicting engagement change for low-frequency users with very few user signals. We believe that insights from this study can be leveraged by search engine companies to detect and intervene search relevance degradation and to prevent long term user engagement drop.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1213–1224},
numpages = {12},
keywords = {user engagement, longitudinal analysis, search relevance, search quality},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488495,
author = {Stadtm\"{u}ller, Steffen and Speiser, Sebastian and Harth, Andreas and Studer, Rudi},
title = {Data-Fu: A Language and an Interpreter for Interaction with Read/Write Linked Data},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488495},
doi = {10.1145/2488388.2488495},
abstract = {An increasing amount of applications build their functionality on the utilisation and manipulation of web resources. Consequently REST gains popularity with a resource-centric interaction architecture that draws its flexibility from links between resources. Linked Data offers a uniform data model for REST with self-descriptive resources that can be leveraged to avoid a manual ad-hoc development of web-based applications. For declaratively specifying interactions between web resources we introduce Data-Fu, a lightweight declarative rule language with state transition systems as formal grounding. Data-Fu enables the development of data-driven applications that facilitate the RESTful manipulation of read/write Linked Data resources. Furthermore, we describe an interpreter for Data-Fu as a general purpose engine that allows to perform described interactions with web resources by orders of magnitude faster than a comparable Linked Data processor.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1225–1236},
numpages = {12},
keywords = {web interaction, rule language, interpreter, rest, linked data},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488496,
author = {Suen, Caroline and Huang, Sandy and Eksombatchai, Chantat and Sosic, Rok and Leskovec, Jure},
title = {NIFTY: A System for Large Scale Information Flow Tracking and Clustering},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488496},
doi = {10.1145/2488388.2488496},
abstract = {The real-time information on news sites, blogs and social networking sites changes dynamically and spreads rapidly through the Web. Developing methods for handling such information at a massive scale requires that we think about how information content varies over time, how it is transmitted, and how it mutates as it spreads.We describe the News Information Flow Tracking, Yay! (NIFTY) system for large scale real-time tracking of "memes" - short textual phrases that travel and mutate through the Web. NIFTY is based on a novel highly-scalable incremental meme-clustering algorithm that efficiently extracts and identifies mutational variants of a single meme. NIFTY runs orders of magnitude faster than our previous Memetracker system, while also maintaining better consistency and quality of extracted memes.We demonstrate the effectiveness of our approach by processing a 20 terabyte dataset of 6.1 billion blog posts and news articles that we have been continuously collecting for the last four years. NIFTY extracted 2.9 billion unique textual phrases and identified more than 9 million memes. Our meme-tracking algorithm was able to process the entire dataset in less than five days using a single machine. Furthermore, we also provide a live deployment of the NIFTY system that allows users to explore the dynamics of online news in near real-time.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1237–1248},
numpages = {12},
keywords = {news media, information cascades, meme-tracking, blogs, social networks, networks of diffusion},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488497,
author = {Szpektor, Idan and Maarek, Yoelle and Pelleg, Dan},
title = {When Relevance is Not Enough: Promoting Diversity and Freshness in Personalized Question Recommendation},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488497},
doi = {10.1145/2488388.2488497},
abstract = {What makes a good question recommendation system for community question-answering sites? First, to maintain the health of the ecosystem, it needs to be designed around answerers, rather than exclusively for askers. Next, it needs to scale to many questions and users, and be fast enough to route a newly-posted question to potential answerers within the few minutes before the asker's patience runs out. It also needs to show each answerer questions that are relevant to his or her interests. We have designed and built such a system for Yahoo! Answers, but realized, when testing it with live users, that it was not enough.We found that those drawing-board requirements fail to capture user's interests. The feature that they really missed was diversity. In other words, showing them just the main topics they had previously expressed interest in was simply too dull. Adding the spice of topics slightly outside the core of their past activities significantly improved engagement. We conducted a large-scale online experiment in production in Yahoo! Answers that showed that recommendations driven by relevance alone perform worse than a control group without question recommendations, which is the current behavior. However, an algorithm promoting both diversity and freshness improved the number of answers by 17%, daily session length by 10%, and had a significant positive impact on peripheral activities such as voting.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1249–1260},
numpages = {12},
keywords = {community question answering, recommender systems, user models},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488498,
author = {Taneva, Bilyana and Cheng, Tao and Chakrabarti, Kaushik and He, Yeye},
title = {Mining Acronym Expansions and Their Meanings Using Query Click Log},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488498},
doi = {10.1145/2488388.2488498},
abstract = {Acronyms are abbreviations formed from the initial components of words or phrases. Acronym usage is becoming more common in web searches, email, text messages, tweets, blogs and posts. Acronyms are typically ambiguous and often disambiguated by context words. Given either just an acronym as a query or an acronym with a few context words, it is immensely useful for a search engine to know the most likely intended meanings, ranked by their likelihood. To support such online scenarios, we study the offline mining of acronyms and their meanings in this paper. For each acronym, our goal is to discover all distinct meanings and for each meaning, compute the expanded string, its popularity score and a set of context words that indicate this meaning. Existing approaches are inadequate for this purpose. Our main insight is to leverage "co-clicks" in search engine query click log to mine expansions of acronyms. There are several technical challenges such as ensuring 1:1 mapping between expansions and meanings, handling of "tail meanings" and extracting context words. We present a novel, end-to-end solution that addresses the above challenges. We further describe how web search engines can leverage the mined information for prediction of intended meaning for queries containing acronyms. Our experiments show that our approach (i) discovers the meanings of acronyms with high precision and recall, (ii) significantly complements existing meanings in Wikipedia and (iii) accurately predicts intended meaning for online queries with over 90% precision.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1261–1272},
numpages = {12},
keywords = {acronym meaning, acronym, click log, acronym expansion},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488499,
author = {Tao, Ke and Abel, Fabian and Hauff, Claudia and Houben, Geert-Jan and Gadiraju, Ujwal},
title = {Groundhog Day: Near-Duplicate Detection on Twitter},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488499},
doi = {10.1145/2488388.2488499},
abstract = {With more than 340~million messages that are posted on Twitter every day, the amount of duplicate content as well as the demand for appropriate duplicate detection mechanisms is increasing tremendously. Yet there exists little research that aims at detecting near-duplicate content on microblogging platforms. We investigate the problem of near-duplicate detection on Twitter and introduce a framework that analyzes the tweets by comparing (i) syntactical characteristics, (ii) semantic similarity, and (iii) contextual information. Our framework provides different duplicate detection strategies that, among others, make use of external Web resources which are referenced from microposts. Machine learning is exploited in order to learn patterns that help identifying duplicate content. We put our duplicate detection framework into practice by integrating it into Twinder, a search engine for Twitter streams. An in-depth analysis shows that it allows Twinder to diversify search results and improve the quality of Twitter search. We conduct extensive experiments in which we (1) evaluate the quality of different strategies for detecting duplicates, (2) analyze the impact of various features on duplicate detection, (3) investigate the quality of strategies that classify to what exact level two microposts can be considered as duplicates and (4) optimize the process of identifying duplicate content on Twitter. Our results prove that semantic features which are extracted by our framework can boost the performance of detecting duplicates.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1273–1284},
numpages = {12},
keywords = {search, twitter, duplicate detection, diversification},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488500,
author = {Thomee, Bart and Rae, Adam},
title = {Uncovering Locally Characterizing Regions within Geotagged Data},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488500},
doi = {10.1145/2488388.2488500},
abstract = {We propose a novel algorithm for uncovering the colloquial boundaries of locally characterizing regions present in collections of labeled geospatial data. We address the problem by first modeling the data using scale-space theory, allowing us to represent it simultaneously across different scales as a family of increasingly smoothed density distributions. We then derive region boundaries by applying localized label weighting and image processing techniques to the scale-space representation of each label. Important insights into the data can be acquired by visualizing the shape and size of the resulting boundaries for each label at multiple scales. We demonstrate our technique operating at scale by discovering the boundaries of the most geospatially salient tags associated with a large collection of georeferenced photos from Flickr and compare our characterizing regions that emerge from the data with those produced by a recent technique from the research literature.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1285–1296},
numpages = {12},
keywords = {spatial analysis, geotagged data, flickr, scale-space theory, region discovery},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488501,
author = {Tsiatas, Alexander and Saniee, Iraj and Narayan, Onuttom and Andrews, Matthew},
title = {Spectral Analysis of Communication Networks Using Dirichlet Eigenvalues},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488501},
doi = {10.1145/2488388.2488501},
abstract = {Good clustering can provide critical insight into potential locations where congestion in a network may occur. A natural measure of congestion for a collection of nodes in a graph is its Cheeger ratio, defined as the ratio of the size of its boundary to its volume. Spectral methods provide effective means to estimate the smallest Cheeger ratio via the spectral gap of the graph Laplacian. Here, we compute the spectral gap of the truncated graph Laplacian, with the so-called Dirichlet boundary condition, for the graphs of a dozen communication networks at the IP-layer, which are subgraphs of the much larger global IP-layer network. We show that i) the Dirichlet spectral gap of these networks is substantially larger than the standard spectral gap and is therefore a better indicator of the true expansion properties of the graph, ii) unlike the standard spectral gap, the Dirichlet spectral gaps of progressively larger subgraphs converge to that of the global network, thus allowing properties of the global network to be efficiently obtained from them, and (iii) the (first two) eigenvectors of the Dirichlet graph Laplacian can be used for spectral clustering with arguably better results than standard spectral clustering. We first demonstrate these results analytically for finite regular trees. We then perform spectral clustering on the IP-layer networks using Dirichlet eigenvectors and show that it yields cuts near the network core, thus creating genuine single-component clusters. This is much better than traditional spectral clustering where several disjoint fragments near the network periphery are liable to be misleadingly classified as a single cluster. Since congestion in communication networks is known to peak at the core due to large-scale curvature and geometry, identification of core congestion and its localization are important steps in analysis and improved engineering of networks. Thus, spectral clustering with Dirichlet boundary condition is seen to be more effective at finding bona-fide bottlenecks and congestion than standard spectral clustering.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1297–1306},
numpages = {10},
keywords = {cheeger ratio, dirichlet eigenvalues, spectral clustering, communication networks},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488502,
author = {Ugander, Johan and Backstrom, Lars and Kleinberg, Jon},
title = {Subgraph Frequencies: Mapping the Empirical and Extremal Geography of Large Graph Collections},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488502},
doi = {10.1145/2488388.2488502},
abstract = {A growing set of on-line applications are generating data that can be viewed as very large collections of small, dense social graphs --- these range from sets of social groups, events, or collaboration projects to the vast collection of graph neighborhoods in large social networks. A natural question is how to usefully define a domain-independent 'coordinate system' for such a collection of graphs, so that the set of possible structures can be compactly represented and understood within a common space. In this work, we draw on the theory of graph homomorphisms to formulate and analyze such a representation, based on computing the frequencies of small induced subgraphs within each graph. We find that the space of subgraph frequencies is governed both by its combinatorial properties --- based on extremal results that constrain all graphs --- as well as by its empirical properties --- manifested in the way that real social graphs appear to lie near a simple one-dimensional curve through this space.We develop flexible frameworks for studying each of these aspects. For capturing empirical properties, we characterize a simple stochastic generative model, a single-parameter extension of Erdos-Renyi random graphs, whose stationary distribution over subgraphs closely tracks the one-dimensional concentration of the real social graph families. For the extremal properties, we develop a tractable linear program for bounding the feasible space of subgraph frequencies by harnessing a toolkit of known extremal graph theory. Together, these two complementary frameworks shed light on a fundamental question pertaining to social graphs: what properties of social graphs are 'social' properties and what properties are 'graph' properties?We conclude with a brief demonstration of how the coordinate system we examine can also be used to perform classification tasks, distinguishing between structures arising from different types of social graphs.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1307–1318},
numpages = {12},
keywords = {subgraph census, induced subgraphs, triadic closure, graph homomorphisms, social networks},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488503,
author = {Vaz de Melo, Pedro Olmo S. and Faloutsos, Christos and Assun\c{c}\~{a}o, Renato and Loureiro, Antonio},
title = {The Self-Feeding Process: A Unifying Model for Communication Dynamics in the Web},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488503},
doi = {10.1145/2488388.2488503},
abstract = {How often do individuals perform a given communication activity in the Web, such as posting comments on blogs or news? Could we have a generative model to create communication events with realistic inter-event time distributions (IEDs)? Which properties should we strive to match? Current literature has seemingly contradictory results for IED: some studies claim good fits with power laws; others with non-homogeneous Poisson processes. Given these two approaches, we ask: which is the correct one? Can we reconcile them all? We show here that, surprisingly, both approaches are correct, being corner cases of the proposed Self-Feeding Process (SFP). We show that the SFP (a) exhibits a unifying power, which generates power law tails (including the so-called "top-concavity" that real data exhibits), as well as short-term Poisson behavior; (b) avoids the "i.i.d. fallacy", which none of the prevailing models have studied before; and (c) is extremely parsimonious, requiring usually only one, and in general, at most two parameters. Experiments conducted on eight large, diverse real datasets (e.g., Youtube and blog comments, e-mails, SMSs, etc) reveal that the SFP mimics their properties very well.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1319–1330},
numpages = {12},
keywords = {inter-event times, generative model, communication dynamics},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488504,
author = {Vi\'{e}gas, Fernanda and Wattenberg, Martin and Hebert, Jack and Borggaard, Geoffrey and Cichowlas, Alison and Feinberg, Jonathan and Orwant, Jon and Wren, Christopher},
title = {Google+Ripples: A Native Visualization of Information Flow},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488504},
doi = {10.1145/2488388.2488504},
abstract = {G+ Ripples is a visualization of information flow that shows users how public posts are shared on Google+. Unlike other social network visualizations, Ripples exists as a "native" visualization: it is directly accessible from public posts on Google+. This unique position leads to both new constraints and new possibilities for design. We describe the visualization technique, which is a new mix of node-and-link and circular treemap metaphors. We then describe user reactions as well as some of the patterns of sharing that are made evident by the Ripples visualization.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1389–1398},
numpages = {10},
keywords = {social media, visualization, information flow},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488505,
author = {Wang, Beidou and Wang, Can and Bu, Jiajun and Chen, Chun and Zhang, Wei Vivian and Cai, Deng and He, Xiaofei},
title = {Whom to Mention: Expand the Diffusion of Tweets by @ Recommendation on Micro-Blogging Systems},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488505},
doi = {10.1145/2488388.2488505},
abstract = {Nowadays, micro-blogging systems like Twitter have become one of the most important ways for information sharing. In Twitter, a user posts a message (tweet) and the others can forward the message (retweet). Mention is a new feature in micro-blogging systems. By mentioning users in a tweet, they will receive notifications and their possible retweets may help to initiate large cascade diffusion of the tweet. To enhance a tweet's diffusion by finding the right persons to mention, we propose in this paper a novel recommendation scheme named as whom-to-mention. Specifically, we present an in-depth study of mention mechanism and propose a recommendation scheme to solve the essential question of whom to mention in a tweet. In this paper, whom-to-mention is formulated as a ranking problem and we try to address several new challenges which are not well studied in the traditional information retrieval tasks. By adopting features including user interest match, content-dependent user relationship and user influence, a machine learned ranking function is trained based on newly defined information diffusion based relevance. The extensive evaluation using data gathered from real users demonstrates the advantage of our proposed algorithm compared with the traditional recommendation methods.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1331–1340},
numpages = {10},
keywords = {recommendation, information retrieval, mention, micro-blogging systems, information diffusion, twitter},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488506,
author = {Wang, Gang and Gill, Konark and Mohanlal, Manish and Zheng, Haitao and Zhao, Ben Y.},
title = {Wisdom in the Social Crowd: An Analysis of Quora},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488506},
doi = {10.1145/2488388.2488506},
abstract = {Efforts such as Wikipedia have shown the ability of user communities to collect, organize and curate information on the Internet. Recently, a number of question and answer (Q&A) sites have successfully built large growing knowledge repositories, each driven by a wide range of questions and answers from its users community. While sites like Yahoo Answers have stalled and begun to shrink, one site still going strong is Quora, a rapidly growing service that augments a regular Q&A system with social links between users. Despite its success, however, little is known about what drives Quora's growth, and how it continues to connect visitors and experts to the right questions as it grows.In this paper, we present results of a detailed analysis of Quora using measurements. We shed light on the impact of three different connection networks (or graphs) inside Quora, a graph connecting topics to users, a social graph connecting users, and a graph connecting related questions. Our results show that heterogeneity in the user and question graphs are significant contributors to the quality of Quora's knowledge base. One drives the attention and activity of users, and the other directs them to a small set of popular and interesting questions.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1341–1352},
numpages = {12},
keywords = {online social networks, graphs, q&a system},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488507,
author = {Wang, Hongning and Song, Yang and Chang, Ming-Wei and He, Xiaodong and White, Ryen W. and Chu, Wei},
title = {Learning to Extract Cross-Session Search Tasks},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488507},
doi = {10.1145/2488388.2488507},
abstract = {Search tasks, comprising a series of search queries serving the same information need, have recently been recognized as an accurate atomic unit for modeling user search intent. Most prior research in this area has focused on short-term search tasks within a single search session, and heavily depend on human annotations for supervised classification model learning. In this work, we target the identification of long-term, or cross-session, search tasks (transcending session boundaries) by investigating inter-query dependencies learned from users' searching behaviors. A semi-supervised clustering model is proposed based on the latent structural SVM framework, and a set of effective automatic annotation rules are proposed as weak supervision to release the burden of manual annotation. Experimental results based on a large-scale search log collected from Bing.com confirms the effectiveness of the proposed model in identifying cross-session search tasks and the utility of the introduced weak supervision signals. Our learned model enables a more comprehensive understanding of users' search behaviors via search logs and facilitates the development of dedicated search-engine support for long-term tasks.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1353–1364},
numpages = {12},
keywords = {weak supervision, cross-session search task, semi-supervised clustering, query log mining},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488508,
author = {Wang, Hongning and Zhai, ChengXiang and Dong, Anlei and Chang, Yi},
title = {Content-Aware Click Modeling},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488508},
doi = {10.1145/2488388.2488508},
abstract = {Click models aim at extracting intrinsic relevance of documents to queries from biased user clicks. One basic modeling assumption made in existing work is to treat such intrinsic relevance as an atomic query-document-specific parameter, which is solely estimated from historical clicks without using any content information about a document or relationship among the clicked/skipped documents under the same query. Due to this overly simplified assumption, existing click models can neither fully explore the information about a document's relevance quality nor make predictions of relevance for any unseen documents.In this work, we proposed a novel Bayesian Sequential State model for modeling the user click behaviors, where the document content and dependencies among the sequential click events within a query are characterized by a set of descriptive features via a probabilistic graphical model. By applying the posterior regularized Expectation Maximization algorithm for parameter learning, we tailor the model to meet specific ranking-oriented properties, e.g., pairwise click preferences, so as to exploit richer information buried in the user clicks. Experiment results on a large set of real click logs demonstrate the effectiveness of the proposed model compared with several state-of-the-art click models.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1365–1376},
numpages = {12},
keywords = {probabilistic graphical model, query log analysis, click modeling},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488509,
author = {Wang, Jian and Zhang, Yi and Posse, Christian and Bhasin, Anmol},
title = {Is It Time for a Career Switch?},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488509},
doi = {10.1145/2488388.2488509},
abstract = {Tenure is a critical factor for an individual to consider when making a job transition. For instance, software engineers make a job transition to senior software engineers in a span of 2 years on average, or it takes for approximately 3 years for realtors to switch to brokers. While most existing work on recommender systems focuses on finding what to recommend to a user, this paper places emphasis on when to make appropriate recommendations and its impact on the item selection in the context of a job recommender system. The approach we propose, however, is general and can be applied to any recommendation scenario where the decision-making process is dependent on the tenure (i.e., the time interval) between successive decisions.Our approach is inspired by the proportional hazards model in statistics. It models the tenure between two successive decisions and related factors. We further extend the model with a hierarchical Bayesian framework to address the problem of data sparsity. The proposed model estimates the likelihood of a user's decision to make a job transition at a certain time, which is denoted as the tenure-based decision probability. New and appropriate evaluation metrics are designed to analyze the model's performance on deciding when is the right time to recommend a job to a user. We validate the soundness of our approach by evaluating it with an anonymous job application dataset across 140+ industries on LinkedIn. Experimental results show that the hierarchical proportional hazards model has better predictability of the user's decision time, which in turn helps the recommender system to achieve higher utility/user satisfaction.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1377–1388},
numpages = {12},
keywords = {tenure, recommender system, hazards model},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488510,
author = {West, Robert and White, Ryen W. and Horvitz, Eric},
title = {From Cookies to Cooks: Insights on Dietary Patterns via Analysis of Web Usage Logs},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488510},
doi = {10.1145/2488388.2488510},
abstract = {Nutrition is a key factor in people's overall health. Hence, understanding the nature and dynamics of population-wide dietary preferences over time and space can be valuable in public health. To date, studies have leveraged small samples of participants via food intake logs or treatment data. We propose a complementary source of population data on nutrition obtained via Web logs. Our main contribution is a spatiotemporal analysis of population-wide dietary preferences through the lens of logs gathered by a widely distributed Web-browser add-on, using the access volume of recipes that users seek via search as a proxy for actual food consumption. We discover that variation in dietary preferences as expressed via recipe access has two main periodic components, one yearly and the other weekly, and that there exist characteristic regional differences in terms of diet within the United States. In a second study, we identify users who show evidence of having made an acute decision to lose weight. We characterize the shifts in interests that they express in their search queries and focus on changes in their recipe queries in particular. Last, we correlate nutritional time series obtained from recipe queries with time-aligned data on hospital admissions, aimed at understanding how behavioral data captured in Web logs might be harnessed to identify potential relationships between diet and acute health problems. In this preliminary study, we focus on patterns of sodium identified in recipes over time and patterns of admission for congestive heart failure, a chronic illness that can be exacerbated by increases in sodium intake.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1399–1410},
numpages = {12},
keywords = {log analysis, public health, behavioral analysis, nutrition},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488511,
author = {White, Ryen W. and Chu, Wei and Hassan, Ahmed and He, Xiaodong and Song, Yang and Wang, Hongning},
title = {Enhancing Personalized Search by Mining and Modeling Task Behavior},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488511},
doi = {10.1145/2488388.2488511},
abstract = {Personalized search systems tailor search results to the current user intent using historic search interactions. This relies on being able to find pertinent information in that user's search history, which can be challenging for unseen queries and for new search scenarios. Building richer models of users' current and historic search tasks can help improve the likelihood of finding relevant content and enhance the relevance and coverage of personalization methods. The task-based approach can be applied to the current user's search history, or as we focus on here, all users' search histories as so-called "groupization" (a variant of personalization whereby other users' profiles can be used to personalize the search experience). We describe a method whereby we mine historic search-engine logs to find other users performing similar tasks to the current user and leverage their on-task behavior to identify Web pages to promote in the current ranking. We investigate the effectiveness of this approach versus query-based matching and finding related historic activity from the current user (i.e., group versus individual). As part of our studies we also explore the use of the on-task behavior of particular user cohorts, such as people who are expert in the topic currently being searched, rather than all other users. Our approach yields promising gains in retrieval performance, and has direct implications for improving personalization in search systems.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1411–1420},
numpages = {10},
keywords = {task modeling, groupization, personalization, task similarity},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488512,
author = {Wu, Qian and Wu, Ling and Liang, Guangtai and Wang, Qianxiang and Xie, Tao and Mei, Hong},
title = {Inferring Dependency Constraints on Parameters for Web Services},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488512},
doi = {10.1145/2488388.2488512},
abstract = {Recently many popular websites such as Twitter and Flickr expose their data through web service APIs, enabling third-party organizations to develop client applications that provide function-alities beyond what the original websites offer. These client appli-cations should follow certain constraints in order to correctly in-teract with the web services. One common type of such constraints is Dependency Constraints on Parameters. Given a web service operation O and its parameters Pi, Pj, these constraints describe the requirement on one parameter Pi that is dependent on the conditions of some other parameter(s) Pj. For example, when requesting the Twitter operation "GET statuses/user_timeline", a user_id parameter must be provided if a screen_name parameter is not provided. Violations of such constraints can cause fatal errors or incorrect results in the client applications. However, these con-straints are often not formally specified and thus not available for automatic verification of client applications. To address this issue, we propose a novel approach, called INDICATOR, to automatically infer dependency constraints on parameters for web services, via a hybrid analysis of heterogeneous web service artifacts, including the service documentation, the service SDKs, and the web services themselves. To evaluate our approach, we applied INDICATOR to infer dependency constraints for four popular web services. The results showed that INDICATOR effectively infers constraints with an average precision of 94.4% and recall of 95.5%.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1421–1432},
numpages = {12},
keywords = {web service, documentation analysis, testing, service sdk, constraints, parameters},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488513,
author = {Xu, Haifeng and Gao, Bin and Yang, Diyi and Liu, Tie-Yan},
title = {Predicting Advertiser Bidding Behaviors in Sponsored Search by Rationality Modeling},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488513},
doi = {10.1145/2488388.2488513},
abstract = {We study how an advertiser changes his/her bid prices in sponsored search, by modeling his/her rationality. Predicting the bid changes of advertisers with respect to their campaign performances is a key capability of search engines, since it can be used to improve the offline evaluation of new advertising technologies and the forecast of future revenue of the search engine. Previous work on advertiser behavior modeling heavily relies on the assumption of perfect advertiser rationality; however, in most cases, this assumption does not hold in practice. Advertisers may be unwilling, incapable, and/or constrained to achieve their best response. In this paper, we explicitly model these limitations in the rationality of advertisers, and build a probabilistic advertiser behavior model from the perspective of a search engine. We then use the expected payoff to define the objective function for an advertiser to optimize given his/her limited rationality. By solving the optimization problem with Monte Carlo, we get a prediction of mixed bid strategy for each advertiser in the next period of time. We examine the effectiveness of our model both directly using real historical bids and indirectly using revenue prediction and click number prediction. Our experimental results based on the sponsored search logs from a commercial search engine show that the proposed model can provide a more accurate prediction of advertiser bid behaviors than several baseline methods.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1433–1444},
numpages = {12},
keywords = {sponsored search, rationality, advertiser modeling, bid prediction},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488514,
author = {Yan, Xiaohui and Guo, Jiafeng and Lan, Yanyan and Cheng, Xueqi},
title = {A Biterm Topic Model for Short Texts},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488514},
doi = {10.1145/2488388.2488514},
abstract = {Uncovering the topics within short texts, such as tweets and instant messages, has become an important task for many content analysis applications. However, directly applying conventional topic models (e.g. LDA and PLSA) on such short texts may not work well. The fundamental reason lies in that conventional topic models implicitly capture the document-level word co-occurrence patterns to reveal topics, and thus suffer from the severe data sparsity in short documents. In this paper, we propose a novel way for modeling topics in short texts, referred as biterm topic model (BTM). Specifically, in BTM we learn the topics by directly modeling the generation of word co-occurrence patterns (i.e. biterms) in the whole corpus. The major advantages of BTM are that 1) BTM explicitly models the word co-occurrence patterns to enhance the topic learning; and 2) BTM uses the aggregated patterns in the whole corpus for learning topics to solve the problem of sparse word co-occurrence patterns at document-level. We carry out extensive experiments on real-world short text collections. The results demonstrate that our approach can discover more prominent and coherent topics, and significantly outperform baseline methods on several evaluation metrics. Furthermore, we find that BTM can outperform LDA even on normal texts, showing the potential generality and wider usage of the new topic model.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1445–1456},
numpages = {12},
keywords = {biterm, topic model, content analysis, short text},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488515,
author = {Yao, Ting and Liu, Yuan and Ngo, Chong-Wah and Mei, Tao},
title = {Unified Entity Search in Social Media Community},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488515},
doi = {10.1145/2488388.2488515},
abstract = {The search for entities is the most common search behavior on the Web, especially in social media communities where entities (such as images, videos, people, locations, and tags) are highly heterogeneous and correlated. While previous research usually deals with these social media entities separately, we are investigating in this paper a unified, multi-level, and correlative entity graph to represent the unstructured social media data, through which various applications (e.g., friend suggestion, personalized image search, image tagging, etc.) can be realized more effectively in one single framework. We regard the social media objects equally as "entities" and all of these applications as "entity search" problem which searches for entities with different types. We first construct a multi-level graph which organizes the heterogeneous entities into multiple levels, with one type of entities as vertices in each level. The edges between graphs pairwisely connect the entities weighted by intra-relations in the same level and inter-links across two different levels distilled from the social behaviors (e.g., tagging, commenting, and joining communities). To infer the strength of intra-relations, we propose a circular propagation scheme, which reinforces the mutual exchange of information across different entity types in a cyclic manner. Based on the constructed unified graph, we explicitly formulate entity search as a global optimization problem in a unified Bayesian framework, in which various applications are efficiently realized. Empirically, we validate the effectiveness of our unified entity graph for various social media applications on million-scale real-world dataset.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1457–1466},
numpages = {10},
keywords = {entity search, image tagging, personalized image search, friend suggestion, social media community},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488516,
author = {Yao, Yuan and Tong, Hanghang and Yan, Xifeng and Xu, Feng and Lu, Jian},
title = {MATRI: A Multi-Aspect and Transitive Trust Inference Model},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488516},
doi = {10.1145/2488388.2488516},
abstract = {Trust inference, which is the mechanism to build new pair-wise trustworthiness relationship based on the existing ones, is a fundamental integral part in many real applications, e.g., e-commerce, social networks, peer-to-peer networks, etc. State-of-the-art trust inference approaches mainly employ the transitivity property of trust by propagating trust along connected users (a.k.a. trust propagation), but largely ignore other important properties, e.g., prior knowledge, multi-aspect, etc.In this paper, we propose a multi-aspect trust inference model by exploring an equally important property of trust, i.e., the multi-aspect property. The heart of our method is to view the problem as a recommendation problem, and hence opens the door to the rich methodologies in the field of collaborative filtering. The proposed multi-aspect model directly characterizes multiple latent factors for each trustor and trustee from the locally-generated trust relationships. Moreover, we extend this model to incorporate the prior knowledge as well as trust propagation to further improve inference accuracy. We conduct extensive experimental evaluations on real data sets, which demonstrate that our method achieves significant improvement over several existing benchmark approaches. Overall, the proposed method (MaTrI) leads to 26.7% - 40.7% improvement over its best known competitors in prediction accuracy; and up to 7 orders of magnitude speedup with linear scalability.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1467–1476},
numpages = {10},
keywords = {multi-aspect property, transitivity property, latent factors, prior knowledge, trust inference},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488517,
author = {Ye, Jihang and Cheng, Hong and Zhu, Zhe and Chen, Minghua},
title = {Predicting Positive and Negative Links in Signed Social Networks by Transfer Learning},

license_type: permissive

branch_name: refs/heads/main

extension: bib