text:
@article{10.1007/s10791-019-09354-z,
author = {Dietz, Laura and Xiong, Chenyan and Dalton, Jeff and Meij, Edgar},
title = {Special Issue on Knowledge Graphs and Semantics in Text Analysis and Retrieval},
year = {2019},
issue_date = {August    2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {22},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-019-09354-z},
doi = {10.1007/s10791-019-09354-z},
journal = {Inf. Retr.},
month = aug,
pages = {229–231},
numpages = {3}
}

@article{10.1007/s10791-018-9348-8,
author = {Sawant, Uma and Garg, Saurabh and Chakrabarti, Soumen and Ramakrishnan, Ganesh},
title = {Neural Architecture for Question Answering Using a Knowledge Graph and Web Corpus},
year = {2019},
issue_date = {August    2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {22},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9348-8},
doi = {10.1007/s10791-018-9348-8},
abstract = {In Web search, entity-seeking queries often trigger a special question answering (QA) system. It may use a parser to interpret the question to a structured query, execute that on a knowledge graph (KG), and return direct entity responses. QA systems based on precise parsing tend to be brittle: minor syntax variations may dramatically change the response. Moreover, KG coverage is patchy. At the other extreme, a large corpus may provide broader coverage, but in an unstructured, unreliable form. We present AQQUCN, a QA system that gracefully combines KG and corpus evidence. AQQUCN accepts a broad spectrum of query syntax, between well-formed questions to short "telegraphic" keyword sequences. In the face of inherent query ambiguities, AQQUCN aggregates signals from KGs and large corpora to directly rank KG entities, rather than commit to one semantic interpretation of the query. AQQUCN models the ideal interpretation as an unobservable or latent variable. Interpretations and candidate entity responses are scored as pairs, by combining signals from multiple convolutional networks that operate collectively on the query, KG and corpus. On four public query workloads, amounting to over 8000 queries with diverse query syntax, we see 5---16% absolute improvement in mean average precision (MAP), compared to the entity ranking performance of recent systems. Our system is also competitive at entity set retrieval, almost doubling F1 scores for challenging short queries.},
journal = {Inf. Retr.},
month = aug,
pages = {324–349},
numpages = {26},
keywords = {Entity ranking, Convolutional network, Question answering, Knowledge graph, Neural network}
}

@article{10.1007/s10791-018-9346-x,
author = {Garigliotti, Dar\'{\i}o and Hasibi, Faegheh and Balog, Krisztian},
title = {Identifying and Exploiting Target Entity Type Information for Ad Hoc Entity Retrieval},
year = {2019},
issue_date = {August    2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {22},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9346-x},
doi = {10.1007/s10791-018-9346-x},
abstract = {Today, the practice of returning entities from a knowledge base in response to search queries has become widespread. One of the distinctive characteristics of entities is that they are typed, i.e., assigned to some hierarchically organized type system (type taxonomy). The primary objective of this paper is to gain a better understanding of how entity type information can be utilized in entity retrieval. We perform this investigation in two settings: firstly, in an idealized "oracle" setting, assuming that we know the distribution of target types of the relevant entities for a given query; and secondly, in a realistic scenario, where target entity types are identified automatically based on the keyword query. We perform a thorough analysis of three main aspects: (i) the choice of type taxonomy, (ii) the representation of hierarchical type information, and (iii) the combination of type-based and term-based similarity in the retrieval model. Using a standard entity search test collection based on DBpedia, we show that type information can significantly and substantially improve retrieval performance, yielding up to 67% relative improvement in terms of NDCG@10 over a strong text-only baseline in an oracle setting. We further show that using automatic target type detection, we can outperform the text-only baseline by 44% in terms of NDCG@10. This is as good as, and sometimes even better than, what is attainable by using explicit target type information provided by humans. These results indicate that identifying target entity types of queries is challenging even for humans and attests to the effectiveness of our proposed automatic approach.},
journal = {Inf. Retr.},
month = aug,
pages = {285–323},
numpages = {39},
keywords = {Entity retrieval, Query understanding, Entity types, Semantic search}
}

@article{10.1007/s10791-018-9345-y,
author = {Nayak, Guruprasad and Dutta, Sourav and Ajwani, Deepak and Nicholson, Patrick and Sala, Alessandra},
title = {Automated Assessment of Knowledge Hierarchy Evolution: Comparing Directed Acyclic Graphs},
year = {2019},
issue_date = {August    2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {22},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9345-y},
doi = {10.1007/s10791-018-9345-y},
abstract = {Automated construction of knowledge hierarchies from huge data corpora is gaining increasing attention in recent years, in order to tackle the infeasibility of manually extracting and semantically linking millions of concepts. As a knowledge hierarchy evolves with these automated techniques, there is a need for measures to assess its temporal evolution, quantifying the similarities between different versions and identifying the relative growth of different subgraphs in the knowledge hierarchy. In this paper, we focus on measures that leverage structural properties of the knowledge hierarchy graph to assess the temporal changes. We propose a principled and scalable similarity measure, based on Katz similarity between concept nodes, for comparing different versions of a knowledge hierarchy, modeled as a generic directed acyclic graph. We present theoretical analysis to depict that the proposed measure accurately captures the salient properties of taxonomic hierarchies, assesses changes in the ordering of nodes, along with the logical subsumption of relationships among concepts. We also present a linear time variant of the measure, and show that our measures, unlike previous approaches, are tunable to cater to diverse application needs. We further show that our measure provides interpretability, thereby identifying the key structural and logical difference in the hierarchies. Experiments on a real DBpedia and biological knowledge hierarchy showcase that our measures accurately capture structural similarity, while providing enhanced scalability and tunability. Also, we demonstrate that the temporal evolution of different subgraphs in this knowledge hierarchy, as captured purely by our structural measure, corresponds well with the known disruptions in the related subject areas.},
journal = {Inf. Retr.},
month = aug,
pages = {256–284},
numpages = {29},
keywords = {Knowledge hierarchy matching, Semantic subsumption, DAG similarity, Taxonomy evaluation, Concept tracking}
}

@article{10.1007/s10791-018-9344-z,
author = {Jimmy and Zuccon, Guido and Koopman, Bevan},
title = {Payoffs and Pitfalls in Using Knowledge-Bases for Consumer Health Search},
year = {2019},
issue_date = {August    2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {22},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9344-z},
doi = {10.1007/s10791-018-9344-z},
abstract = {Consumer health search (CHS) is a challenging domain with vocabulary mismatch and considerable domain expertise hampering peoples' ability to formulate effective queries. We posit that using knowledge bases for query reformulation may help alleviate this problem. How to exploit knowledge bases for effective CHS is nontrivial, involving a swathe of key choices and design decisions (many of which are not explored in the literature). Here we rigorously empirically evaluate the impact these different choices have on retrieval effectiveness.
 A state-of-the-art knowledge-base retrieval model--the Entity Query Feature Expansion model--was used to evaluate these choices, which include: which knowledge base to use (specialised vs. general purpose), how to construct the knowledge base, how to extract entities from queries and map them to entities in the knowledge base, what part of the knowledge base to use for query expansion, and if to augment the knowledge base search process with relevance feedback.
 While knowledge base retrieval has been proposed as a solution for CHS, this paper delves into the finer details of doing this effectively, highlighting both payoffs and pitfalls. It aims to provide some lessons to others in advancing the state-of-the-art in CHS.},
journal = {Inf. Retr.},
month = aug,
pages = {350–394},
numpages = {45},
keywords = {Consumer health search, Query expansion, Knowledge graph, Knowledge base}
}

@article{10.1007/s10791-018-9343-0,
author = {Macavaney, Sean and Yates, Andrew and Cohan, Arman and Soldaini, Luca and Hui, Kai and Goharian, Nazli and Frieder, Ophir},
title = {Overcoming Low-Utility Facets for Complex Answer Retrieval},
year = {2019},
issue_date = {August    2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {22},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9343-0},
doi = {10.1007/s10791-018-9343-0},
abstract = {Many questions cannot be answered simply; their answers must include numerous nuanced details and context. Complex Answer Retrieval (CAR) is the retrieval of answers to such questions. These questions can be constructed from a topic entity (e.g., `cheese') and a facet (e.g., `health effects'). While topic matching has been thoroughly explored, we observe that some facets use general language that is unlikely to appear verbatim in answers, exhibiting low utility. In this work, we present an approach to CAR that identifies and addresses low-utility facets. First, we propose two estimators of facet utility: the hierarchical structure of CAR queries, and facet frequency information from training data. Then, to improve the retrieval performance on low-utility headings, we include entity similarity scores using embeddings trained from a CAR knowledge graph, which captures the context of facets. We show that our methods are effective by applying them to two leading neural ranking techniques, and evaluating them on the TREC CAR dataset. We find that our approach perform significantly better than the unmodified neural ranker and other leading CAR techniques, yielding state-of-the-art results. We also provide a detailed analysis of our results, verify that low-utility facets are indeed difficult to match, and that our approach improves the performance for these difficult queries.},
journal = {Inf. Retr.},
month = aug,
pages = {395–418},
numpages = {24},
keywords = {Reranking, Neural information retrieval, Complex answer retrieval, Knowledge graphs}
}

@article{10.1007/s10791-018-9342-1,
author = {Rastogi, Pushpendre and Poliak, Adam and Lyzinski, Vince and Durme, Benjamin},
title = {Neural Variational Entity Set Expansion for Automatically Populated Knowledge Graphs},
year = {2019},
issue_date = {August    2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {22},
number = {3–4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9342-1},
doi = {10.1007/s10791-018-9342-1},
abstract = {We propose Neural variational set expansion to extract actionable information from a noisy knowledge graph (KG) and propose a general approach for increasing the interpretability of recommendation systems. We demonstrate the usefulness of applying a variational autoencoder to the Entity set expansion task based on a realistic automatically generated KG.},
journal = {Inf. Retr.},
month = aug,
pages = {232–255},
numpages = {24},
keywords = {Unsupervised learning, Variational autoencoder, Product of experts (POE), Set expansion, Cold start recommendation, Content based recommendation}
}

@article{10.1007/s10791-019-09352-1,
author = {Boratto, Ludovico and Kaltenbrunner, Andreas and Stilo, Giovanni},
title = {Guest Editorial: Social Media for Personalization and Search},
year = {2019},
issue_date = {April     2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {22},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-019-09352-1},
doi = {10.1007/s10791-019-09352-1},
journal = {Inf. Retr.},
month = apr,
pages = {1–3},
numpages = {3}
}

@article{10.1007/s10791-018-9341-2,
author = {Kulshrestha, Juhi and Eslami, Motahhare and Messias, Johnnatan and Zafar, Muhammad Bilal and Ghosh, Saptarshi and Gummadi, Krishna P. and Karahalios, Karrie},
title = {Search Bias Quantification: Investigating Political Bias in Social Media and Web Search},
year = {2019},
issue_date = {April     2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {22},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9341-2},
doi = {10.1007/s10791-018-9341-2},
abstract = {Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources--input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems--Twitter social media search and Google web search--for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.},
journal = {Inf. Retr.},
month = apr,
pages = {188–227},
numpages = {40},
keywords = {Search bias, Web search, Social media search, Sources of search bias, Search bias quantification, Political bias inference}
}

@article{10.1007/s10791-018-9339-9,
author = {Naini, Kaweh Djafari and Kawase, Ricardo and Kanhabua, Nattiya and Nieder\'{e}e, Claudia and Altingovde, Ismail Sengor},
title = {Those Were the Days: Learning to Rank Social Media Posts for Reminiscence},
year = {2019},
issue_date = {April     2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {22},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9339-9},
doi = {10.1007/s10791-018-9339-9},
abstract = {Social media posts are a great source for life summaries aggregating activities, events, interactions and thoughts of the last months or years.
They can be used for personal reminiscence as well as for keeping track with developments in the lives of not-so-close friends. One of the core challenges of automatically creating such summaries is to decide which posts are memorable, i.e., should be considered for retention and which ones to forget. To address this challenge, we design and conduct user evaluation studies and construct a corpus that captures human expectations towards content retention. We analyze this corpus to identify a small set of seed features that are most likely to characterize memorable posts. Next, we compile a broader set of features that are leveraged to build general and personalized machine-learning models to rank posts for retention. By applying feature selection, we identify a compact yet effective subset of these features. The models trained with the presented feature sets outperform the baseline models exploiting an intuitive set of temporal and social features.},
journal = {Inf. Retr.},
month = apr,
pages = {159–187},
numpages = {29},
keywords = {Personalized ranking, Letor, Social features, Personalization, Feature selection, Learning to rank, Content retention, Social media, Facebook}
}

@article{10.1007/s10791-018-9338-x,
author = {Bennacer Seghouani, Nac\'{e}ra and Jipmo, Coriane Nana and Quercini, Gianluca},
title = {Determining the Interests of Social Media Users: Two Approaches},
year = {2019},
issue_date = {April     2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {22},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9338-x},
doi = {10.1007/s10791-018-9338-x},
abstract = {Although social media platforms serve diverse purposes, from social and professional networking to photo sharing and blogging, people frequently use them to share the thoughts and opinions and most importantly, their interests (e.g., politics, economy, sports). Understanding the interests of social media users is key to many applications that need to characterize them to recommend some services and find other individuals with similar interests. In this paper, we propose two approaches to the automatic determination of the interests of social media users. The first, that we named Frisk, is an unsupervised multilingual approach that determines the interests of a user from the explicit meaning of the words that occur in the user's posts. The second, that we termed Ascertain, is a supervised approach that resorts to the hidden dimensions of the words that several studies indicated to be capable of revealing some of the psychological processes and personality traits of a person. In our evaluation, that we performed on two datasets obtained from Twitter, we show that Frisk is capable of inferring the interests in a multilingual context with good accuracy and that the psychological dimensions used by Ascertain are also good predictors of a user's interests.},
journal = {Inf. Retr.},
month = apr,
pages = {129–158},
numpages = {30},
keywords = {Interest classification, Twitter user profile, Personality}
}

@article{10.1007/s10791-018-9337-y,
author = {Zarrinkalam, Fattane and Kahani, Mohsen and Bagheri, Ebrahim},
title = {User Interest Prediction over Future Unobserved Topics on Social Networks},
year = {2019},
issue_date = {April     2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {22},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9337-y},
doi = {10.1007/s10791-018-9337-y},
abstract = {The accurate prediction of users' future interests on social networks allows one to perform future planning by studying how users will react if certain topics emerge in the future. It can improve areas such as targeted advertising and the efficient delivery of services. Despite the importance of predicting user future interests on social networks, existing works mainly focus on identifying user current interests and little work has been done on the prediction of user potential interests in the future. There have been work that attempt to identify a user future interests, however they cannot predict user interests with regard to new topics since these topics have never received any feedback from users in the past. In this paper, we propose a framework that works on the basis of temporal evolution of user interests and utilizes semantic information from knowledge bases such as Wikipedia to predict user future interests and overcome the cold item problem. Through extensive experiments on a real-world Twitter dataset, we demonstrate the effectiveness of our approach in predicting future interests of users compared to state-of-the-art baselines. Moreover, we further show that the impact of our work is especially meaningful when considered in case of cold items.},
journal = {Inf. Retr.},
month = apr,
pages = {93–128},
numpages = {36},
keywords = {User modeling, Twitter, Wikipedia category hierarchy, User interest prediction}
}

@article{10.1007/s10791-018-9336-z,
author = {Eberhard, Lukas and Trattner, Christoph and Atzmueller, Martin},
title = {Predicting Trading Interactions in an Online Marketplace through Location-Based and Online Social Networks},
year = {2019},
issue_date = {April     2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {22},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9336-z},
doi = {10.1007/s10791-018-9336-z},
abstract = {Link prediction is a prominent research direction e.g., for inferring upcoming interactions to be used in recommender systems. Although this problem of predicting links between users has been extensively studied in the past, research investigating this issue simultaneously in multiplex networks is rather rare so far. This is the focus of this paper. We investigate the extent to which trading interactions between sellers and buyers within an online marketplace platform can be predicted based on three different but overlapping networks--an online social network, a location-based social network and a trading network. In particular, we conducted the study in the context of the virtual world Second Life. For that, we crawled according data of the online social network, user information of the location-based social network obtained by specialized bots, and we extracted purchases of the trading network. Overall, we generated and used 57 topological and homophilic features in different constellations to predict trading interactions between user pairs. We focused on both unsupervised as well as supervised learning methods. For supervised learning, we achieved accuracy values up to $$92.5%$$92.5%, for unsupervised learning we obtained nDCG values up to over $$97%$$97% and MAP values up to $$75%$$75%.},
journal = {Inf. Retr.},
month = apr,
pages = {55–92},
numpages = {38},
keywords = {Location-based and online social networks, Seller, Buyer, Link prediction, Second life, Supervised and unsupervised learning}
}

@article{10.1007/s10791-018-9335-0,
author = {Monteserin, Ariel and Armentano, Marcelo G.},
title = {Influence Me! Predicting Links to Influential Users},
year = {2019},
issue_date = {April     2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {22},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9335-0},
doi = {10.1007/s10791-018-9335-0},
abstract = {In addition to being in contact with friends, online social networks are commonly used as a source of information, suggestions and recommendations from members of the community. Whenever we accept a suggestion or perform any action because it was recommended by a "friend", we are being influenced by him/her. For this reason, it is useful for users seeking for interesting information to identify and connect to this kind of influential users.
 In this context, we propose an approach to predict links to influential users. Compared to approaches that identify general influential users in a network, our approach seeks to identify users who might have some kind of influence to individual (target) users. To carry out this goal, we adapted an influence maximization algorithm to find new influential users from the set of current influential users of the target user. Moreover, we compared the results obtained with different metrics for link prediction and analyzed in which context these metrics obtained better results.
},
journal = {Inf. Retr.},
month = apr,
pages = {32–54},
numpages = {23},
keywords = {Social influence, Link prediction, Social networks}
}

@article{10.1007/s10791-018-9333-2,
author = {Cucchiarelli, Alessandro and Morbidoni, Christian and Stilo, Giovanni and Velardi, Paola},
title = {A Topic Recommender for Journalists},
year = {2019},
issue_date = {April     2019},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {22},
number = {1–2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9333-2},
doi = {10.1007/s10791-018-9333-2},
abstract = {The way in which people gather information about events and form their own opinion on them has changed dramatically with the advent of social media. For many readers, the news gathered from online sources has become an opportunity to share points of view and information within micro-blogging platforms such as Twitter, mainly aimed at satisfying their communication needs. Furthermore, the need to deepen the aspects related to news stimulates a demand for additional information which is often met through online encyclopedias, such as Wikipedia. This behaviour has also influenced the way in which journalists write their articles, requiring a careful assessment of what actually interests the readers. The goal of this paper is to present a recommender system, What to Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest. The basic idea is to characterize an event according to the echo it receives in online news sources and associate it with the corresponding readers' communicative and informative patterns, detected through the analysis of Twitter and Wikipedia, respectively. Our methodology temporally aligns the results of this analysis and recommends the concepts that emerge as topics of interest from Twitter and Wikipedia, either not covered or poorly covered in the published news articles.},
journal = {Inf. Retr.},
month = apr,
pages = {4–31},
numpages = {28},
keywords = {Event detection, Wikipedia, Temporal mining, Online News, Twitter, Recommender systems}
}

@article{10.1007/s10791-018-9329-y,
author = {Ganguly, Debasis and Jones, Gareth J.},
title = {A Non-Parametric Topical Relevance Model},
year = {2018},
issue_date = {October   2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9329-y},
doi = {10.1007/s10791-018-9329-y},
abstract = {An information retrieval (IR) system can often fail to retrieve relevant documents due to the incomplete specification of information need in the user's query. Pseudo-relevance feedback (PRF) aims to improve IR effectiveness by exploiting potentially relevant aspects of the information need present in the documents retrieved in an initial search. Standard PRF approaches utilize the information contained in these top ranked documents from the initial search with the assumption that documents as a whole are relevant to the information need. However, in practice, documents are often multi-topical where only a portion of the documents may be relevant to the query. In this situation, exploitation of the topical composition of the top ranked documents, estimated with statistical topic modeling based approaches, can potentially be a useful cue to improve PRF effectiveness. The key idea behind our PRF method is to use the term-topic and the document-topic distributions obtained from topic modeling over the set of top ranked documents to re-rank the initially retrieved documents. The objective is to improve the ranks of documents that are primarily composed of the relevant topics expressed in the information need of the query. Our RF model can further be improved by making use of non-parametric topic modeling, where the number of topics can grow according to the document contents, thus giving the RF model the capability to adjust the number of topics based on the content of the top ranked documents. We empirically validate our topic model based RF approach on two document collections of diverse length and topical composition characteristics: (1) ad-hoc retrieval using the TREC 6-8 and the TREC Robust '04 dataset, and (2) tweet retrieval using the TREC Microblog '11 dataset. Results indicate that our proposed approach increases MAP by up to 9% in comparison to the results obtained with an LDA based language model (for initial retrieval) coupled with the relevance model (for feedback). Moreover, the non-parametric version of our proposed approach is shown to be more effective than its parametric counterpart due to its advantage of adapting the number of topics, improving results by up to 5.6% of MAP compared to the parametric version.},
journal = {Inf. Retr.},
month = oct,
pages = {449–479},
numpages = {31},
keywords = {Latent Dirichlet allocation, Non-parametric topic modeling, Pseudo-relevance feedback, Query-likelihood model, Relevance model}
}

@article{10.1007/s10791-018-9328-z,
author = {Foka, Amalia F.},
title = {An Artist Ranking System Based on Social Media Mining},
year = {2018},
issue_date = {October   2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9328-z},
doi = {10.1007/s10791-018-9328-z},
abstract = {Currently users on social media post their opinion and feelings about almost everything. This online behavior has led to numerous applications where social media data are used to measure public opinion in a similar way as a poll or a survey. In this paper, we will present an application of social media mining for the art market. To the best of our knowledge, this will be the first attempt to mine social media to extract quantitative and qualitative data for the art market. Although there are previous works on analyzing and predicting other markets, these methodologies cannot be applied directly to the art market. In our proposed methodology, artists will be treated as brands. That is, we will mine Twitter posts that mention specific artists' names and attempt to rank artists in a similar manner as brand equity and awareness would be measured. The particularities of the art market are considered mainly in the construction of a topic-specific user network where user expertise and influence is evaluated and later used to rank artists. The proposed ranking system is evaluated against two other available systems to identify the advantages it can offer.},
journal = {Inf. Retr.},
month = oct,
pages = {410–448},
numpages = {39},
keywords = {User influence, Art market, Social media mining, Social media user network, Topic identification, User expertise}
}

@article{10.1007/s10791-018-9327-0,
author = {Zamani, Hamed and Shakery, Azadeh},
title = {A Language Model-Based Framework for Multi-Publisher Content-Based Recommender Systems},
year = {2018},
issue_date = {October   2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-018-9327-0},
doi = {10.1007/s10791-018-9327-0},
abstract = {The rapid growth of the Web has increased the difficulty of finding the information that can address the users' information needs. A number of recommendation approaches have been developed to tackle this problem. The increase in the number of data providers has necessitated the development of multi-publisher recommender systems; systems that include more than one item/data provider. In such environments, preserving the privacy of both publishers and subscribers is a key and challenging point. In this paper, we propose a multi-publisher framework for recommender systems based on a client---server architecture, which preserves the privacy of both data providers and subscribers. We develop our framework as a content-based filtering system using the statistical language modeling framework. We also introduce AUTO, a simple yet effective threshold optimization algorithm, to find a dissemination threshold for making acceptance and rejection decisions for new published documents. We further propose a language model sketching technique to reduce the network traffic between servers and clients in the proposed framework. Extensive experiments using the TREC-9 Filtering Track and the CLEF 2008-09 INFILE Track collections indicate the effectiveness of the proposed models in both single- and multi-publisher settings.},
journal = {Inf. Retr.},
month = oct,
pages = {369–409},
numpages = {41},
keywords = {Language models, Threshold optimization, Privacy preservation, Content-based recommender system, Multi-publisher recommendation, Adaptive filtering}
}

@article{10.1007/s10791-017-9326-6,
author = {Zingla, Meriem Amina and Latiri, Chiraz and Mulhem, Philippe and Berrut, Catherine and Slimani, Yahya},
title = {Hybrid Query Expansion Model for Text and Microblog Information Retrieval},
year = {2018},
issue_date = {August    2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9326-6},
doi = {10.1007/s10791-017-9326-6},
abstract = {Query expansion (QE) is an important process in information retrieval applications that improves the user query and helps in retrieving relevant results. In this paper, we introduce a hybrid query expansion model (HQE) that investigates how external resources can be combined to association rules mining and used to enhance expansion terms generation and selection. The HQE model can be processed in different configurations, starting from methods based on association rules and combining it with external knowledge. The HQE model handles the two main phases of a QE process, namely: the candidate terms generation phase and the selection phase. We propose for the first phase, statistical, semantic and conceptual methods to generate new related terms for a given query. For the second phase, we introduce a similarity measure, ESAC, based on the Explicit Semantic Analysis that computes the relatedness between a query and the set of candidate terms. The performance of the proposed HQE model is evaluated within two experimental validations. The first one addresses the tweet search task proposed by TREC Microblog Track 2011 and an ad-hoc IR task related to the hard topics of the TREC Robust 2004. The second experimental validation concerns the tweet contextualization task organized by INEX 2014. Global results highlighted the effectiveness of our HQE model and of association rules mining for QE combined with external resources.},
journal = {Inf. Retr.},
month = aug,
pages = {337–367},
numpages = {31},
keywords = {dbpedia, Tweets search, Query expansion, Tweet contextualization, Ad-hoc IR task, Information retrieval, Association rules, Explicit Semantic Analysis, wikipedia}
}

@article{10.1007/s10791-017-9325-7,
author = {Hasanain, Maram and Suwaileh, Reem and Elsayed, Tamer and Kutlu, Mucahid and Almerekhi, Hind},
title = {EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets},
year = {2018},
issue_date = {August    2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9325-7},
doi = {10.1007/s10791-017-9325-7},
abstract = {This article introduces a new language-independent approach for creating a large-scale high-quality test collection of tweets that supports multiple information retrieval (IR) tasks without running a shared-task campaign. The adopted approach (demonstrated over Arabic tweets) designs the collection around significant (i.e., popular) events, which enables the development of topics that represent frequent information needs of Twitter users for which rich content exists. That inherently facilitates the support of multiple tasks that generally revolve around events, namely event detection, ad-hoc search, timeline generation, and real-time summarization. The key highlights of the approach include diversifying the judgment pool via interactive search and multiple manually-crafted queries per topic, collecting high-quality annotations via crowd-workers for relevancy and in-house annotators for novelty, filtering out low-agreement topics and inaccessible tweets, and providing multiple subsets of the collection for better availability. Applying our methodology on Arabic tweets resulted in EveTAR, the first freely-available tweet test collection for multiple IR tasks. EveTAR includes a crawl of 355M Arabic tweets and covers 50 significant events for which about 62K tweets were judged with substantial average inter-annotator agreement (Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating existing algorithms in the respective tasks. Results indicate that the new collection can support reliable ranking of IR systems that is comparable to similar TREC collections, while providing strong baseline results for future studies over Arabic tweets.},
journal = {Inf. Retr.},
month = aug,
pages = {307–336},
numpages = {30},
keywords = {Microblogs, Dialects, Real-time summarization, Evaluation, Ad-hoc search, Timeline generation, Event detection, Twitter}
}

@article{10.1007/s10791-017-9324-8,
author = {Kotlerman, Lili and Dagan, Ido and Kurland, Oren},
title = {Clustering Small-Sized Collections of Short Texts},
year = {2018},
issue_date = {August    2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9324-8},
doi = {10.1007/s10791-017-9324-8},
abstract = {The need to cluster small text corpora composed of a few hundreds of short texts rises in various applications; e.g., clustering top-retrieved documents based on their snippets. This clustering task is challenging due to the vocabulary mismatch between short texts and the insufficient corpus-based statistics (e.g., term co-occurrence statistics) due to the corpus size. We address this clustering challenge using a framework that utilizes a set of external knowledge resources that provide information about term relations. Specifically, we use information induced from the resources to estimate similarity between terms and produce term clusters. We also utilize the resources to expand the vocabulary used in the given corpus and thus enhance term clustering. We then project the texts in the corpus onto the term clusters to cluster the texts. We evaluate various instantiations of the proposed framework by varying the term clustering method used, the approach of projecting the texts onto the term clusters, and the way of applying external knowledge resources. Extensive empirical evaluation demonstrates the merits of our approach with respect to applying clustering algorithms directly on the text corpus, and using state-of-the-art co-clustering and topic modeling methods.},
journal = {Inf. Retr.},
month = aug,
pages = {273–306},
numpages = {34},
keywords = {Clustering, Short text similarities, Clustering short texts}
}

@article{10.1007/s10791-017-9320-z,
author = {Carvalho, Cristiano and Moura, Edleno Silva and Veloso, Adriano and Ziviani, Nivio},
title = {Website Replica Detection with Distant Supervision},
year = {2018},
issue_date = {August    2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9320-z},
doi = {10.1007/s10791-017-9320-z},
abstract = {Duplicate content on the Web occurs within the same website or across multiple websites. The latter is mainly associated with the existence of website replicas--sites that are perceptibly similar. Replication may be accidental, intentional or malicious, but no matter the reason, search engines suffer greatly either from unnecessarily storing and moving duplicate data, or from providing search results that do not offer real value to the users. In this paper, we model the detection of website replicas as a pairwise classification problem with distant supervision. That is, (heuristically) finding obvious replica and non-replica cases is trivial, but learning effective classifiers requires a representative set of non-obvious labeled examples, which are hard to obtain. We employ efficient Expectation-Maximization (EM) algorithms in order to find non-obvious examples from obvious ones, enlarging the training-set and improving the classifiers iteratively. Our classifiers employ association rules, being thus incrementally updated as the EM process iterates, making our algorithms time-efficient. Experiments show that: (1) replicas are fully eliminated at a false-positive rate lower than 0.005, incurring in + 19% reduction in the number of duplicate URLs, (2) reduction increases to + 21% by using our site-level algorithms in conjunction with existing URL-level algorithms, and (3) our classifiers are more than two orders of magnitude faster than semi-supervised alternative solutions.},
journal = {Inf. Retr.},
month = aug,
pages = {253–272},
numpages = {20},
keywords = {Replica detection, Expectation-Maximization, Distant Supervision}
}

@article{10.1007/s10791-017-9323-9,
author = {Craswell, Nick and Croft, W. Bruce and Rijke, Maarten and Guo, Jiafeng and Mitra, Bhaskar},
title = {Neural Information Retrieval: Introduction to the Special Issue},
year = {2018},
issue_date = {June      2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9323-9},
doi = {10.1007/s10791-017-9323-9},
journal = {Inf. Retr.},
month = jun,
pages = {107–110},
numpages = {4}
}

@article{10.1007/s10791-017-9321-y,
author = {Onal, Kezban Dilek and Zhang, Ye and Altingovde, Ismail Sengor and Rahman, Md Mustafizur and Karagoz, Pinar and Braylan, Alex and Dang, Brandon and Chang, Heng-Lu and Kim, Henna and Mcnamara, Quinten and Angert, Aaron and Banner, Edward and Khetan, Vivek and Mcdonnell, Tyler and Nguyen, An Thanh and Xu, Dan and Wallace, Byron C. and Rijke, Maarten and Lease, Matthew},
title = {Neural Information Retrieval: At the End of the Early Years},
year = {2018},
issue_date = {June      2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9321-y},
doi = {10.1007/s10791-017-9321-y},
abstract = {A recent "third wave" of neural network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, work in this area is often referred to as deep learning. Recent years have witnessed an explosive growth of research into NN-based approaches to information retrieval (IR). A significant body of work has now been created. In this paper, we survey the current landscape of Neural IR research, paying special attention to the use of learned distributed representations of textual units. We highlight the successes of neural IR thus far, catalog obstacles to its wider adoption, and suggest potentially promising directions for future research.},
journal = {Inf. Retr.},
month = jun,
pages = {111–182},
numpages = {72},
keywords = {Semantic compositionality, Semantic matching, Recurrent neural network, Deep learning, Word embedding, Search engine, Neural network, Distributed representation}
}

@article{10.1007/s10791-017-9319-5,
author = {Yang, Xiao and Macdonald, Craig and Ounis, Iadh},
title = {Using Word Embeddings in Twitter Election Classification},
year = {2018},
issue_date = {June      2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9319-5},
doi = {10.1007/s10791-017-9319-5},
abstract = {Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, as well as the parameters of the word embedding training process, namely the context window size, the dimensionality and the number of negative samples, on the attained classification performance. By comparing the classification results of word embedding models that have been trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data should align with the Twitter classification dataset both in data type and time period to achieve significantly better performance compared to baselines such as SVM with TF-IDF. Moreover, by evaluating the results of word embedding models trained using various context window sizes and dimensionalities, we find that large context window and dimension sizes are preferable to improve the performance. However, the number of negative samples parameter does not significantly affect the performance of the CNN classifiers. Our experimental results also show that choosing the correct word embedding model for use with CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings. Finally, for out-of-vocabulary (OOV) words that are not available in the learned word embedding models, we show that a simple OOV strategy to randomly initialise the OOV words without any prior knowledge is sufficient to attain a good classification performance among the current OOV strategies (e.g. a random initialisation using statistics of the pre-trained word embedding models).},
journal = {Inf. Retr.},
month = jun,
pages = {183–207},
numpages = {25},
keywords = {Word2vec, CNN, Election classification, Word embedding, Twitter}
}

@article{10.1007/s10791-017-9318-6,
author = {Carrara, Fabio and Esuli, Andrea and Fagni, Tiziano and Falchi, Fabrizio and Moreo Fern\'{a}ndez, Alejandro},
title = {Picture It in Your Mind: Generating High Level Visual Representations from Textual Descriptions},
year = {2018},
issue_date = {June      2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9318-6},
doi = {10.1007/s10791-017-9318-6},
abstract = {In this paper we tackle the problem of image search when the query is a short textual description of the image the user is looking for. We choose to implement the actual search process as a similarity search in a visual feature space, by learning to translate a textual query into a visual representation. Searching in the visual feature space has the advantage that any update to the translation model does not require to reprocess the (typically huge) image collection on which the search is performed. We propose various neural network models of increasing complexity that learn to generate, from a short descriptive text, a high level visual representation in a visual feature space such as the pool5 layer of the ResNet-152 or the fc6---fc7 layers of an AlexNet trained on ILSVRC12 and Places databases. The Text2Vis models we explore include (1) a relatively simple regressor network relying on a bag-of-words representation for the textual descriptors, (2) a deep recurrent network that is sensible to word order, and (3) a wide and deep model that combines a stacked LSTM deep network with a wide regressor network. We compare the models we propose with other search strategies, also including textual search methods that exploit state-of-the-art caption generation models to index the image collection.},
journal = {Inf. Retr.},
month = jun,
pages = {208–229},
numpages = {22},
keywords = {Image retrieval, Text representation, Cross-media retrieval}
}

@article{10.1007/s10791-017-9317-7,
author = {Wang, Dongjing and Deng, Shuiguang and Xu, Guandong},
title = {Sequence-Based Context-Aware Music Recommendation},
year = {2018},
issue_date = {June      2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {2–3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9317-7},
doi = {10.1007/s10791-017-9317-7},
abstract = {Contextual factors greatly affect users' preferences for music, so they can benefit music recommendation and music retrieval. However, how to acquire and utilize the contextual information is still facing challenges. This paper proposes a novel approach for context-aware music recommendation, which infers users' preferences for music, and then recommends music pieces that fit their real-time requirements. Specifically, the proposed approach first learns the low dimensional representations of music pieces from users' music listening sequences using neural network models. Based on the learned representations, it then infers and models users' general and contextual preferences for music from users' historical listening records. Finally, music pieces in accordance with user's preferences are recommended to the target user. Extensive experiments are conducted on real world datasets to compare the proposed method with other state-of-the-art recommendation methods. The results demonstrate that the proposed method significantly outperforms those baselines, especially on sparse data.},
journal = {Inf. Retr.},
month = jun,
pages = {230–252},
numpages = {23},
keywords = {Recommender systems, Context-aware, Embedding, Sequence-based, Neural network}
}

@article{10.1007/s10791-017-9322-x,
author = {Imhof, Melanie and Braschler, Martin},
title = {A Study of Untrained Models for Multimodal Information Retrieval},
year = {2018},
issue_date = {February  2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9322-x},
doi = {10.1007/s10791-017-9322-x},
abstract = {Operational multimodal information retrieval systems have to deal with increasingly complex document collections and queries that are composed of a large set of textual and non-textual modalities such as ratings, prices, timestamps, geographical coordinates, etc. The resulting combinatorial explosion of modality combinations makes it intractable to treat each modality individually and to obtain suitable training data. As a consequence, instead of finding and training new models for each individual modality or combination of modalities, it is crucial to establish unified models, and fuse their outputs in a robust way. Since the most popular weighting schemes for textual retrieval have in the past generalized well to many retrieval tasks, we demonstrate how they can be adapted to be used with non-textual modalities, which is a first step towards finding such a unified model. We demonstrate that the popular weighting scheme BM25 is suitable to be used for multimodal IR systems and analyze the underlying assumptions of the BM25 formula with respect to merging modalities under the so-called raw-score merging hypothesis, which requires no training. We establish a multimodal baseline for two multimodal test collections, show how modalities differ with respect to their contribution to relevance and the difficulty of treating modalities with overlapping information. Our experiments demonstrate that our multimodal baseline with no training achieves a significantly higher retrieval effectiveness than using just the textual modality for the social book search 2016 collection and lies in the range of a trained multimodal approach using the optimal linear combination of the modality scores.},
journal = {Inf. Retr.},
month = feb,
pages = {81–106},
numpages = {26},
keywords = {BM25, Multimodal information retrieval, Raw-score merging hypothesis}
}

@article{10.1007/s10791-017-9316-8,
author = {Yang, Grace Hui and Dong, Xuchu and Luo, Jiyun and Zhang, Sicong},
title = {Session Search Modeling by Partially Observable Markov Decision Process},
year = {2018},
issue_date = {February  2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9316-8},
doi = {10.1007/s10791-017-9316-8},
abstract = {Session search, the task of document retrieval for a series of queries in a session, has been receiving increasing attention from the information retrieval research community. Session search exhibits the properties of rich user-system interactions and temporal dependency. These properties lead to our proposal of using partially observable Markov decision process to model session search. On the basis of a design choice schema for states, actions and rewards, we evaluate different combinations of these choices over the TREC 2012 and 2013 session track datasets. According to the experimental results, practical design recommendations for using PODMP in session search are discussed.},
journal = {Inf. Retr.},
month = feb,
pages = {56–80},
numpages = {25},
keywords = {Session search, Dynamic IR modeling, POMDP}
}

@article{10.1007/s10791-017-9314-x,
author = {Costa, Gianni and Ortale, Riccardo},
title = {Machine Learning Techniques for XML (Co-)Clustering by Structure-Constrained Phrases},
year = {2018},
issue_date = {February  2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9314-x},
doi = {10.1007/s10791-017-9314-x},
abstract = {A new method is proposed for clustering XML documents by structure-constrained phrases. It is implemented by three machine-learning approaches previously unexplored in the XML domain, namely non-negative matrix (tri-)factorization, co-clustering and automatic transactional clustering. A novel class of XML features approximately captures structure-constrained phrases as n-grams contextualized by root-to-leaf paths. Experiments over real-world benchmark XML corpora show that the effectiveness of the three approaches improves with contextualized n-grams of suitable length. This confirms the validity of the devised method from multiple clustering perspectives. Two approaches overcome in effectiveness several state-of-the-art competitors. The scalability of the three approaches is investigated, too.},
journal = {Inf. Retr.},
month = feb,
pages = {24–55},
numpages = {32},
keywords = {Semi-structured data analysis, XML (co-)clustering by structure and nested text, Structure-constrained phrases, XML, Contextualized n-grams}
}

@article{10.1007/s10791-017-9313-y,
author = {Ganguly, Debasis and Jones, Gareth J. and Ram\'{\i}rez-De-La-Cruz, Aar\'{o}n and Ram\'{\i}rez-De-La-Rosa, Gabriela and Villatoro-Tello, Esa\'{u}},
title = {Retrieving and Classifying Instances of Source Code Plagiarism},
year = {2018},
issue_date = {February  2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {21},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9313-y},
doi = {10.1007/s10791-017-9313-y},
abstract = {Automatic detection of source code plagiarism is an important research field for both the commercial software industry and within the research community. Existing methods of plagiarism detection primarily involve exhaustive pairwise document comparison, which does not scale well for large software collections. To achieve scalability, we approach the problem from an information retrieval (IR) perspective. We retrieve a ranked list of candidate documents in response to a pseudo-query representation constructed from each source code document in the collection. The challenge in source code document retrieval is that the standard bag-of-words (BoW) representation model for such documents is likely to result in many false positives being retrieved, because of the use of identical programming language specific constructs and keywords. To address this problem, we make use of an abstract syntax tree (AST) representation of the source code documents. While the IR approach is efficient, it is essentially unsupervised in nature. To further improve its effectiveness, we apply a supervised classifier (pre-trained with features extracted from sample plagiarized source code pairs) on the top ranked retrieved documents. We report experiments on the SOCO-2014 dataset comprising 12K Java source files with almost 1M lines of code. Our experiments confirm that the AST based approach produces significantly better retrieval effectiveness than a standard BoW representation, i.e., the AST based approach is able to identify a higher number of plagiarized source code documents at top ranks in response to a query source code document. The supervised classifier, trained on features extracted from sample plagiarized source code pairs, is shown to effectively filter and thus further improve the ranked list of retrieved candidate plagiarized documents.},
journal = {Inf. Retr.},
month = feb,
pages = {1–23},
numpages = {23},
keywords = {Source code plagiarism detection, Lexical, Field based indexing and retrieval, Document representation, Structural and stylistic features}
}

@article{10.1007/s10791-017-9312-z,
author = {Bellog\'{\i}n, Alejandro and Castells, Pablo and Cantador, Iv\'{a}n},
title = {Statistical Biases in Information Retrieval Metrics for Recommender Systems},
year = {2017},
issue_date = {December  2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9312-z},
doi = {10.1007/s10791-017-9312-z},
abstract = {There is an increasing consensus in the Recommender Systems community that the dominant error-based evaluation metrics are insufficient, and mostly inadequate, to properly assess the practical effectiveness of recommendations. Seeking to evaluate recommendation rankings--which largely determine the effective accuracy in matching user needs--rather than predicted rating values, Information Retrieval metrics have started to be applied for the evaluation of recommender systems. In this paper we analyse the main issues and potential divergences in the application of Information Retrieval methodologies to recommender system evaluation, and provide a systematic characterisation of experimental design alternatives for this adaptation. We lay out an experimental configuration framework upon which we identify and analyse specific statistical biases arising in the adaptation of Information Retrieval metrics to recommendation tasks, namely sparsity and popularity biases. These biases considerably distort the empirical measurements, hindering the interpretation and comparison of results across experiments. We develop a formal characterisation and analysis of the biases upon which we analyse their causes and main factors, as well as their impact on evaluation metrics under different experimental configurations, illustrating the theoretical findings with empirical evidence. We propose two experimental design approaches that effectively neutralise such biases to a large extent. We report experiments validating our proposed experimental variants, and comparing them to alternative approaches and metrics that have been defined in the literature with similar or related purposes.},
journal = {Inf. Retr.},
month = dec,
pages = {606–634},
numpages = {29},
keywords = {Popularity bias, Sparsity bias, Cranfield, Evaluation, Recommender systems}
}

@article{10.1007/s10791-017-9311-0,
author = {Liu, Mengwen and Fang, Yi and Choulos, Alexander G. and Park, Dae Hoon and Hu, Xiaohua},
title = {Product Review Summarization through Question Retrieval and Diversification},
year = {2017},
issue_date = {December  2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9311-0},
doi = {10.1007/s10791-017-9311-0},
abstract = {Product reviews have become an important resource for customers before they make purchase decisions. However, the abundance of reviews makes it difficult for customers to digest them and make informed choices. In our study, we aim to help customers who want to quickly capture the main idea of a lengthy product review before they read the details. In contrast with existing work on review analysis and document summarization, we aim to retrieve a set of real-world user questions to summarize a review. In this way, users would know what questions a given review can address and they may further read the review only if they have similar questions about the product. Specifically, we design a two-stage approach which consists of question selection and question diversification. For question selection phase, we first employ probabilistic retrieval models to locate candidate questions that are relevant to a given review. A Recurrent Neural Network Encoder---Decoder is utilized to measure the "answerability" of questions to a review. We then design a set function to re-rank the questions with the goal of rewarding diversity in the final question set. The set function satisfies submodularity and monotonicity, which results in an efficient greedy algorithm of submodular optimization. Evaluation on product reviews from two categories shows that the proposed approach is effective for discovering meaningful questions that are representative of individual reviews.},
journal = {Inf. Retr.},
month = dec,
pages = {575–605},
numpages = {31},
keywords = {Sequence-to-sequence learning, Diversity, Review summarization, Question retrieval}
}

@article{10.1007/s10791-017-9307-9,
author = {Zhuang, Xu and Zhu, Yan and Chang, Chin-Chen and Peng, Qiang and Khurshid, Faisal},
title = {A Unified Score Propagation Model for Web Spam Demotion Algorithm},
year = {2017},
issue_date = {December  2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9307-9},
doi = {10.1007/s10791-017-9307-9},
abstract = {Web spam pages exploit the biases of search engine algorithms to get higher than their deserved rankings in search results by using several types of spamming techniques. Many web spam demotion algorithms have been developed to combat spam via the use of the web link structure, from which the goodness or badness score of each web page is evaluated. Those scores are then used to identify spam pages or punish their rankings in search engine results. However, most of the published spam demotion algorithms differ from their base models by only very limited improvements and still suffer from some common score manipulation methods. The lack of a general framework for this field makes the task of designing high-performance spam demotion algorithms very inefficient. In this paper, we propose a unified score propagation model for web spam demotion algorithms by abstracting the score propagation process of relevant models with a forward score propagation function and a backward score propagation function, each of which can further be expressed as three sub-functions: a splitting function, an accepting function and a combination function. On the basis of the proposed model, we develop two new web spam demotion algorithms named Supervised Forward and Backward score Ranking (SFBR) and Unsupervised Forward and Backward score Ranking (UFBR). Our experiments, conducted on three large-scale public datasets, show that (1) SFBR is very robust and apparently outperforms other algorithms and (2) UFBR can obtain results comparable to some well-known supervised algorithms in the spam demotion task even if the UFBR is unsupervised.},
journal = {Inf. Retr.},
month = dec,
pages = {547–574},
numpages = {28},
keywords = {Spam detection, Web ranking algorithms, Web scoring system, Web spam demotion}
}

@article{10.1007/s10791-017-9315-9,
author = {Eickhoff, Carsten and Gwizdka, Jacek and Hauff, Claudia and He, Jiyin},
title = {Introduction to the Special Issue on Search as Learning},
year = {2017},
issue_date = {October   2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9315-9},
doi = {10.1007/s10791-017-9315-9},
journal = {Inf. Retr.},
month = oct,
pages = {399–402},
numpages = {4}
}

@article{10.1007/s10791-017-9310-1,
author = {Azpiazu, Ion Madrazo and Dragovic, Nevena and Pera, Maria Soledad and Fails, Jerry Alan},
title = {Online Searching and Learning: YUM and Other Search Tools for Children and Teachers},
year = {2017},
issue_date = {October   2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9310-1},
doi = {10.1007/s10791-017-9310-1},
abstract = {Information discovery tasks using online search tools are performed on a regular basis by school-age children. However, these tools are not necessarily designed to both explicitly facilitate the retrieval of resources these young users can comprehend and aid low-literacy searchers. This is of particular concern for educational environments, as there is an inherent expectation that these tools facilitate effective learning. In this manuscript we present an initial assessment conducted over (1) children-oriented search tools based on queries generated by K-9 students, analyzing features such as readability and adequacy of retrieved results, and (2) tools used by teachers in their classrooms, analyzing their main purpose and target audience's age range. Among the examined tools, we include YouUnderstood.Me, an enhanced search environment, which is the result of our ongoing efforts on the development of a search environment tailored to 5-15 year-olds that can foster learning through the retrieval of materials that not only satisfy the information needs of these users but also match their reading abilities. The results of these studies highlight the fact that search results presented to children have average reading levels that do not match the target audience. In addition, tools oriented to teachers do not go beyond showing the progress of their students, and seldomly provide a simple way of retrieving class contents that fit current needs of students. These facts further showcase the need for developing a dual environment oriented to both teachers and students.},
journal = {Inf. Retr.},
month = oct,
pages = {524–545},
numpages = {22},
keywords = {Children, Personalization, Teachers, Search as learning}
}

@article{10.1007/s10791-017-9308-8,
author = {Karanam, Saraschandra and Jorge-Botana, Guillermo and Olmos, Ricardo and Oostendorp, Herre},
title = {The Role of Domain Knowledge in Cognitive Modeling of Information Search},
year = {2017},
issue_date = {October   2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9308-8},
doi = {10.1007/s10791-017-9308-8},
abstract = {Computational cognitive models developed so far do not incorporate individual differences in domain knowledge in predicting user clicks on search result pages. We address this problem using a cognitive model of information search which enables us to use two semantic spaces having a low (non-expert semantic space) and a high (expert semantic space) amount of medical and health related information to represent respectively low and high knowledge of users in this domain. We also investigated two different processes along which one can gain a larger amount of knowledge in a domain: an evolutionary and a common core process. Simulations of model click behavior on difficult information search tasks and subsequent matching with actual behavioral data from users (divided into low and high domain knowledge groups based on a domain knowledge test) were conducted. Results showed that the efficacy of modeling for high domain knowledge participants (in terms of the number of matches between the model predictions and the actual user clicks on search result pages) was higher with the expert semantic space compared to the non-expert semantic space while for low domain knowledge participants it was the other way around. When the process of knowledge acquisition was taken into account, the effect of using a semantic space based on high domain knowledge was significant only for high domain knowledge participants, irrespective of the knowledge acquisition process. The implications of these outcomes for support tools that can be built based on these models are discussed.},
journal = {Inf. Retr.},
month = oct,
pages = {456–479},
numpages = {24},
keywords = {Domain knowledge, Knowledge acquisition, Corpus, Information search, Semantic space, Cognitive modeling}
}

@article{10.1007/s10791-017-9306-x,
author = {Kodama, Christie and St. Jean, Beth and Subramaniam, Mega and Taylor, Natalie Greene},
title = {There's a Creepy Guy on the Other End at Google! Engaging Middle School Students in a Drawing Activity to Elicit Their Mental Models of Google},
year = {2017},
issue_date = {October   2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9306-x},
doi = {10.1007/s10791-017-9306-x},
abstract = {Although youth are increasingly going online to fulfill their needs for information, many youth struggle with information and digital literacy skills, such as the abilities to conduct a search and assess the credibility of online information. Ideally, these skills encompass an accurate and comprehensive understanding of the ways in which a system, such as a Web search engine, functions. In order to investigate youths' conceptions of the Google search engine, a drawing activity was conducted with 26 HackHealth after-school program participants to elicit their mental models of Google. The findings revealed that many participants personified Google and emphasized anthropomorphic elements, computing equipment, and/or connections (such as cables, satellites and antennas) in their drawings. Far fewer participants focused their drawings on the actual Google interface or on computer code. Overall, their drawings suggest a limited understanding of Google and the ways in which it actually works. However, an understanding of youths' conceptions of Google can enable educators to better tailor their digital literacy instruction efforts and can inform search engine developers and search engine interface designers in making the inner workings of the engine more transparent and their output more trustworthy to young users. With a better understanding of how Google works, young users will be better able to construct effective queries, assess search results, and ultimately find relevant and trustworthy information that will be of use to them.},
journal = {Inf. Retr.},
month = oct,
pages = {403–432},
numpages = {30},
keywords = {Information retrieval, Digital youth, Youth information seeking, Internet searching, Mental models, Adolescents}
}

@article{10.1007/s10791-017-9305-y,
author = {Lu, Yihan and Hsiao, I-Han},
title = {Personalized Information Seeking Assistant (PiSA): From Programming Information Seeking to Learning},
year = {2017},
issue_date = {October   2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9305-y},
doi = {10.1007/s10791-017-9305-y},
abstract = {Online programming discussion forums have grown increasingly and formed sizable repositories of problem-solving solutions. In this paper, we investigate programming learners' information seeking behaviors in online discussion forums, and provide visual navigational support to facilitate information seeking. We design engines to collect students' information seeking behaviors, and model these behaviors with sequence pattern mining techniques. The results show that programming learners indeed seek for information from discussion forums by actively search and read progressively according to course schedule topics. Advanced students consistently perform query refinements, examine search results and commit to read, however, novices do not. Finally, according to the lessons learned, we propose, design and evaluate Personalized Information Seeking Assistant system to help query refinement by summarizing the search results and to provide social-based browsing history. Findings suggest that paying attention to the query history may lead to further reading events, which subsequently resulting in potential learning activities.},
journal = {Inf. Retr.},
month = oct,
pages = {433–455},
numpages = {23},
keywords = {Computing education, Novice programming learning, Behavior modeling, Information seeking behavior}
}

@article{10.1007/s10791-017-9304-z,
author = {Knight, Simon and Rienties, Bart and Littleton, Karen and Tempelaar, Dirk and Mitsui, Matthew and Shah, Chirag},
title = {The Orchestration of a Collaborative Information Seeking Learning Task},
year = {2017},
issue_date = {October   2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9304-z},
doi = {10.1007/s10791-017-9304-z},
abstract = {The paper describes our novel perspective on `searching to learn' through collaborative information seeking (CIS). We describe this perspective, which motivated empirical work to `orchestrate' a CIS searching to learn session. The work is described through the lens of orchestration, an approach which brings to the fore the ways in which: background context--including practical classroom constraints, and theoretical perspective; actors--including the educators, researchers, and technologies; and activities that are to be completed, are brought into alignment. The orchestration is exemplified through the description of research work designed to explore a pedagogically salient construct (epistemic cognition), in a particular institutional setting. Evaluation of the session indicated satisfaction with the orchestration from students, with written feedback indicating reflection from them on features of the orchestration. We foreground this approach to demonstrate the potential of orchestration as a design approach for researching and implementing CIS as a `searching to learn' context.},
journal = {Inf. Retr.},
month = oct,
pages = {480–505},
numpages = {26},
keywords = {Searching to learn, Educational technology, Computer supported collaborative learning, Collaborative information seeking}
}

@article{10.1007/s10791-017-9303-0,
author = {Syed, Rohail and Collins-Thompson, Kevyn},
title = {Optimizing Search Results for Human Learning Goals},
year = {2017},
issue_date = {October   2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9303-0},
doi = {10.1007/s10791-017-9303-0},
abstract = {While past research has shown that learning outcomes can be influenced by the amount of effort students invest during the learning process, there has been little research into this question for scenarios where people use search engines to learn. In fact, learning-related tasks represent a significant fraction of the time users spend using Web search, so methods for evaluating and optimizing search engines to maximize learning are likely to have broad impact. Thus, we introduce and evaluate a retrieval algorithm designed to maximize educational utility for a vocabulary learning task, in which users learn a set of important keywords for a given topic by reading representative documents on diverse aspects of the topic. Using a crowdsourced pilot study, we compare the learning outcomes of users across four conditions corresponding to rankings that optimize for different levels of keyword density. We find that adding keyword density to the retrieval objective gave significant learning gains on some topics, with higher levels of keyword density generally corresponding to more time spent reading per word, and stronger learning gains per word read. We conclude that our approach to optimizing search ranking for educational utility leads to retrieved document sets that ultimately may result in more efficient learning of important concepts.},
journal = {Inf. Retr.},
month = oct,
pages = {506–523},
numpages = {18},
keywords = {Intrinsic diversity, Assessment of learning in search, Retrieval models and ranking}
}

@article{10.1007/s10791-017-9302-1,
author = {Campos, Ricardo and Dias, Ga\"{e}l and Jorge, Al\'{\i}pio M\'{a}rio and Nunes, C\'{e}lia},
title = {Identifying Top Relevant Dates for Implicit Time Sensitive Queries},
year = {2017},
issue_date = {August    2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9302-1},
doi = {10.1007/s10791-017-9302-1},
abstract = {Despite a clear improvement of search and retrieval temporal applications, current search engines are still mostly unaware of the temporal dimension. Indeed, in most cases, systems are limited to offering the user the chance to restrict the search to a particular time period or to simply rely on an explicitly specified time span. If the user is not explicit in his/her search intents (e.g., "philip seymour hoffman") search engines may likely fail to present an overall historic perspective of the topic. In most such cases, they are limited to retrieving the most recent results. One possible solution to this shortcoming is to understand the different time periods of the query. In this context, most state-of-the-art methodologies consider any occurrence of temporal expressions in web documents and other web data as equally relevant to an implicit time sensitive query. To approach this problem in a more adequate manner, we propose in this paper the detection of relevant temporal expressions to the query. Unlike previous metadata and query log-based approaches, we show how to achieve this goal based on information extracted from document content. However, instead of simply focusing on the detection of the most obvious date we are also interested in retrieving the set of dates that are relevant to the query. Towards this goal, we define a general similarity measure that makes use of co-occurrences of words and years based on corpus statistics and a classification methodology that is able to identify the set of top relevant dates for a given implicit time sensitive query, while filtering out the non-relevant ones. Through extensive experimental evaluation, we mean to demonstrate that our approach offers promising results in the field of temporal information retrieval (T-IR), as demonstrated by the experiments conducted over several baselines on web corpora collections.},
journal = {Inf. Retr.},
month = aug,
pages = {363–398},
numpages = {36},
keywords = {Relevant temporal expressions, Temporal query understanding, Implicit time sensitive queries, Temporal information retrieval}
}

@article{10.1007/s10791-017-9301-2,
author = {P\"{a}\"{a}kk\"{o}nen, Teemu and Kek\"{a}l\"{a}inen, Jaana and Keskustalo, Heikki and Azzopardi, Leif and Maxwell, David and J\"{a}rvelin, Kalervo},
title = {Validating Simulated Interaction for Retrieval Evaluation},
year = {2017},
issue_date = {August    2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9301-2},
doi = {10.1007/s10791-017-9301-2},
abstract = {
A searcher's interaction with a retrieval system consists of actions such as query formulation, search result list interaction and document interaction. The simulation of searcher interaction has recently gained momentum in the analysis and evaluation of interactive information retrieval (IIR). However, a key issue that has not yet been adequately addressed is the validity of such IIR simulations and whether they reliably predict the performance obtained by a searcher across the session. The aim of this paper is to determine the validity of the common interaction model (CIM) typically used for simulating multi-query sessions. We focus on search result interactions, i.e., inspecting snippets, examining documents and deciding when to stop examining the results of a single query, or when to stop the whole session. To this end, we run a series of simulations grounded by real world behavioral data to show how accurate and responsive the model is to various experimental conditions under which the data were produced.
 We then validate on a second real world data set derived under similar experimental conditions. We seek to predict cumulated gain across the session. We find that the interaction model with a query-level stopping strategy based on consecutive non-relevant snippets leads to the highest prediction accuracy, and lowest deviation from ground truth, around 9 to 15% depending on the experimental conditions. To our knowledge, the present study is the first validation effort of the CIM that shows that the model's acceptance and use is justified within IIR evaluations. We also identify and discuss ways to further improve the CIM and its behavioral parameters for more accurate simulations.},
journal = {Inf. Retr.},
month = aug,
pages = {338–362},
numpages = {25},
keywords = {Session-based evaluation, IR interaction, Simulation}
}

@article{10.1007/s10791-017-9300-3,
author = {Zhou, Xing and Ding, Lixin and Li, Zhaokui and Wan, Runze},
title = {Collaborator Recommendation in Heterogeneous Bibliographic Networks Using Random Walks},
year = {2017},
issue_date = {August    2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9300-3},
doi = {10.1007/s10791-017-9300-3},
abstract = {The increasingly growing popularity of the collaboration among researchers and the increasing information overload in big scholarly data make it imperative to develop a collaborator recommendation system for researchers to find potential partners. Existing works always study this task as a link prediction problem in a homogeneous network with a single object type (i.e., author) and a single link type (i.e., co-authorship). However, a real-world academic social network often involves several object types, e.g., papers, terms, and venues, as well as multiple relationships among different objects. This paper proposes a RWR-CR (standing for random walk with restart-based collaborator recommendation) algorithm in a heterogeneous bibliographic network towards this problem. First, we construct a heterogeneous network with multiple types of nodes and links with a simplified network structure by removing the citing paper nodes. Then, two importance measures are used to weight edges in the network, which will bias a random walker's behaviors. Finally, we employ a random walk with restart to retrieve relevant authors and output an ordered recommendation list in terms of ranking scores.
 Experimental results on DBLP and hep-th datasets demonstrate the effectiveness of our methodology and its promising performance in collaborator prediction.},
journal = {Inf. Retr.},
month = aug,
pages = {317–337},
numpages = {21},
keywords = {Collaborator recommendation, Random walk with restart, Link prediction, Heterogeneous bibliographic network, Importance measure}
}

@article{10.1007/s10791-017-9309-7,
author = {Hawking, David and Moffat, Alistair and Trotman, Andrew},
title = {Efficiency in Information Retrieval: Introduction to Special Issue},
year = {2017},
issue_date = {June      2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9309-7},
doi = {10.1007/s10791-017-9309-7},
journal = {Inf. Retr.},
month = jun,
pages = {169–171},
numpages = {3}
}

@article{10.1007/s10791-017-9299-5,
author = {Tolosa, Gabriel and Feuerstein, Esteban and Becchetti, Luca and Marchetti-Spaccamela, Alberto},
title = {Performance Improvements for Search Systems Using an Integrated Cache of Lists + Intersections},
year = {2017},
issue_date = {June      2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9299-5},
doi = {10.1007/s10791-017-9299-5},
abstract = {Modern information retrieval systems use several levels of caching to speedup computation by exploiting frequent, recent or costly data used in the past. Previous studies show that the use of caching techniques is crucial in search engines, as it helps reducing query response times and processing workloads on search servers. In this work we propose and evaluate a static cache that acts simultaneously as list and intersection cache, offering a more efficient way of handling cache space. We also use a query resolution strategy that takes advantage of the existence of this cache to reorder the query execution sequence. In addition, we propose effective strategies to select the term pairs that should populate the cache. We also represent the data in cache in both raw and compressed forms and evaluate the differences between them using different configurations of cache sizes. The results show that the proposed Integrated Cache outperforms the standard posting lists cache in most of the cases, taking advantage not only of the intersection cache but also the query resolution strategy.},
journal = {Inf. Retr.},
month = jun,
pages = {172–198},
numpages = {27},
keywords = {Caching, Information retrieval systems, Integrated cache, Performance improvement}
}

@article{10.1007/s10791-017-9298-6,
author = {Daoud, Caio Moura and Moura, Edleno Silva and Fernandes, David and Silva, Altigran Soares and Rossi, Cristian and Carvalho, Andre},
title = {Waves: A Fast Multi-Tier Top-k Query Processing Algorithm},
year = {2017},
issue_date = {June      2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9298-6},
doi = {10.1007/s10791-017-9298-6},
abstract = {In this paper, we present Waves, a novel document-at-a-time algorithm for fast computing of top-k query results in search systems. The Waves algorithm uses multi-tier indexes for processing queries. It performs successive tentative evaluations of results which we call waves. Each wave traverses the index, starting from a specific tier level i. Each wave i may insert only those documents that occur in that tier level into the answer. After processing a wave, the algorithm checks whether the answer achieved might be changed by successive waves or not. A new wave is started only if it has a chance of changing the top-k scores. We show through experiments that such lazy query processing strategy results in smaller query processing times when compared to previous approaches proposed in the literature. We present experiments to compare Waves' performance to the state-of-the-art document-at-a-time query processing methods that preserve top-k results and show scenarios where the method can be a good alternative algorithm for computing top-k results.},
journal = {Inf. Retr.},
month = jun,
pages = {292–316},
numpages = {25},
keywords = {Query processing, Information retrieval, Search system}
}

@article{10.1007/s10791-017-9297-7,
author = {Gagie, Travis and Hartikainen, Aleksi and Karhu, Kalle and K\"{a}rkk\"{a}inen, Juha and Navarro, Gonzalo and Puglisi, Simon J. and Sir\'{e}n, Jouni},
title = {Document Retrieval on Repetitive String Collections},
year = {2017},
issue_date = {June      2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9297-7},
doi = {10.1007/s10791-017-9297-7},
abstract = {Most of the fastest-growing string collections today are repetitive, that is, most of the constituent documents are similar to many others. As these collections keep growing, a key approach to handling them is to exploit their repetitiveness, which can reduce their space usage by orders of magnitude. We study the problem of indexing repetitive string collections in order to perform efficient document retrieval operations on them. Document retrieval problems are routinely solved by search engines on large natural language collections, but the techniques are less developed on generic string collections. The case of repetitive string collections is even less understood, and there are very few existing solutions. We develop two novel ideas, interleaved LCPs and precomputed document lists, that yield highly compressed indexes solving the problem of document listing (find all the documents where a string appears), top-k document retrieval (find the k documents where a string appears most often), and document counting (count the number of documents where a string appears). We also show that a classical data structure supporting the latter query becomes highly compressible on repetitive data. Finally, we show how the tools we developed can be combined to solve ranked conjunctive and disjunctive multi-term queries under the simple $${textsf{tf}}{textsf{-}}{textsf{idf}}$$tf-idf model of relevance. We thoroughly evaluate the resulting techniques in various real-life repetitiveness scenarios, and recommend the best choices for each case.},
journal = {Inf. Retr.},
month = jun,
pages = {253–291},
numpages = {39},
keywords = {Document retrieval on strings, Suffix trees and arrays, Repetitive string collections}
}

@article{10.1007/s10791-016-9291-5,
author = {Lin, Jimmy and Trotman, Andrew},
title = {The Role of Index Compression in Score-at-a-Time Query Evaluation},
year = {2017},
issue_date = {June      2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-016-9291-5},
doi = {10.1007/s10791-016-9291-5},
abstract = {This paper explores the performance of top k document retrieval with score-at-a-time query evaluation on impact-ordered indexes in main memory. To better understand execution efficiency in the context of modern processor architectures, we examine the role of index compression on query evaluation latency. Experiments include compressing postings with variable byte encoding, Simple-8b, variants of the QMX compression scheme, as well as a condition that is less often considered--no compression. Across four web test collections, we find that the highest query evaluation speed is achieved by simply leaving the postings lists uncompressed, although the performance advantage over a state-of-the-art compression scheme is relatively small and the index is considerably larger. We explain this finding in terms of the design of modern processor architectures: Index segments with high impact scores are usually short and inherently benefit from cache locality. Index segments with lower impact scores may be quite long, but modern architectures have sufficient memory bandwidth (coupled with prefetching) to "keep up" with the processor. Our results highlight the importance of "architecture affinity" when designing high-performance search engines.},
journal = {Inf. Retr.},
month = jun,
pages = {199–220},
numpages = {22}
}

@article{10.1007/s10791-016-9290-6,
author = {Kim, Yubin and Callan, Jamie and Culpepper, J. Shane and Moffat, Alistair},
title = {Efficient Distributed Selective Search},
year = {2017},
issue_date = {June      2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-016-9290-6},
doi = {10.1007/s10791-016-9290-6},
abstract = {Simulation and analysis have shown that selective search can reduce the cost of large-scale distributed information retrieval.
By partitioning the collection into small topical shards, and then using a resource ranking algorithm to choose a subset of shards to search for each query, fewer postings are evaluated.
In this paper we extend the study of selective search into new areas using a fine-grained simulation, examining the difference in efficiency when term-based and sample-based resource selection algorithms are used; measuring the effect of two policies for assigning index shards to machines; and exploring the benefits of index-spreading and mirroring as the number of deployed machines is varied. Results obtained for two large datasets and four large query logs confirm that selective search is significantly more efficient than conventional distributed search architectures and can handle higher query rates. Furthermore, we demonstrate that selective search can be tuned to avoid bottlenecks, and thus maximize usage of the underlying computer hardware.},
journal = {Inf. Retr.},
month = jun,
pages = {221–252},
numpages = {32},
keywords = {Selective search, Load balancing, Efficiency, Distributed search}
}

@article{10.1007/s10791-017-9296-8,
author = {Kristianto, Giovanni Yoko and Topi\'{c}, Goran and Aizawa, Akiko},
title = {Utilizing Dependency Relationships between Math Expressions in Math IR},
year = {2017},
issue_date = {April     2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9296-8},
doi = {10.1007/s10791-017-9296-8},
abstract = {Current mathematical search systems allow math expressions within a document to be queried using math expressions and keywords. To accept such queries, math search systems must index both math expressions and textual information in documents. Each indexed math expression is usually associated with all the words in its surrounding context within a given window size. However, we found that this context is often ineffective for explaining math expressions in scientific papers. The meaning of an expression is usually defined in the early part of a document, and the meaning of each symbol contained in the expression can be useful for explaining the entire expression. This explanation may not be captured within the context of a math expression, unless we set the context to have a very wide window size. However, widening the window size also increases the proportion of words that are unrelated to the expression. This paper proposes the use of dependency relationships between math expressions to enrich the textual information of each expression. We examine the influence of this enrichment in a math search system. The experimental results show that significantly better precision can be obtained using the enriched textual information rather than the math expressions' own textual information. This indicates that the enrichment of textual information for each math expression using dependency relationships enhances the math search system.},
journal = {Inf. Retr.},
month = apr,
pages = {132–167},
numpages = {36},
keywords = {Dependency graph, Mathematical information retrieval, Mathematical expression encoding, Contextual information}
}

@article{10.1007/s10791-017-9295-9,
author = {Ebesu, Travis and Fang, Yi},
title = {Neural Semantic Personalized Ranking for Item Cold-Start Recommendation},
year = {2017},
issue_date = {April     2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9295-9},
doi = {10.1007/s10791-017-9295-9},
abstract = {Recommender systems help users deal with information overload and enjoy a personalized experience on the Web. One of the main challenges in these systems is the item cold-start problem which is very common in practice since modern online platforms have thousands of new items published every day. Furthermore, in many real-world scenarios, the item recommendation tasks are based on users' implicit preference feedback such as whether a user has interacted with an item. To address the above challenges, we propose a probabilistic modeling approach called Neural Semantic Personalized Ranking (NSPR) to unify the strengths of deep neural network and pairwise learning. Specifically, NSPR tightly couples a latent factor model with a deep neural network to learn a robust feature representation from both implicit feedback and item content, consequently allowing our model to generalize to unseen items. We demonstrate NSPR's versatility to integrate various pairwise probability functions and propose two variants based on the Logistic and Probit functions. We conduct a comprehensive set of experiments on two real-world public datasets and demonstrate that NSPR significantly outperforms the state-of-the-art baselines.},
journal = {Inf. Retr.},
month = apr,
pages = {109–131},
numpages = {23},
keywords = {Pairwise learning, Recommender systems, Implicit feedback, Deep neural network}
}

@article{10.1007/s10791-017-9293-y,
author = {Liu, Mengwen and Ding, Wanying and Park, Dae Hoon and Fang, Yi and Yan, Rui and Hu, Xiaohua},
title = {Which Used Product is More Sellable? A Time-Aware Approach},
year = {2017},
issue_date = {April     2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {2},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9293-y},
doi = {10.1007/s10791-017-9293-y},
abstract = {A number of online marketplaces enable customers to buy or sell used products, which raises the need for ranking tools to help them find desirable items among a huge pool of choices. To the best of our knowledge, no prior work in the literature has investigated the task of used product ranking which has its unique characteristics compared with regular product ranking. While there exist a few ranking metrics (e.g., price, conversion probability) that measure the "goodness" of a product, they do not consider the time factor, which is crucial in used product trading due to the fact that each used product is often unique while new products are usually abundant in supply or quantity. In this paper, we introduce a novel time-aware metric--"sellability", which is defined as the time duration for a used item to be traded, to quantify the value of it. In order to estimate the "sellability" values for newly generated used products and to present users with a ranked list of the most relevant results, we propose a combined Poisson regression and listwise ranking model. The model has a good property in fitting the distribution of "sellability". In addition, the model is designed to optimize loss functions for regression and ranking simultaneously, which is different from previous approaches that are conventionally learned with a single cost function, i.e., regression or ranking. We evaluate our approach in the domain of used vehicles. Experimental results show that the proposed model can improve both regression and ranking performance compared with non-machine learning and machine learning baselines.},
journal = {Inf. Retr.},
month = apr,
pages = {81–108},
numpages = {28},
keywords = {Used product ranking, Learning to rank, Implicit feedback}
}

@article{10.1007/s10791-017-9294-x,
author = {Liu, Yiqun and Nie, Jian-Yun and Chang, Yi},
title = {Constructing Click Models for Search Users},
year = {2017},
issue_date = {February  2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-017-9294-x},
doi = {10.1007/s10791-017-9294-x},
journal = {Inf. Retr.},
month = feb,
pages = {1–3},
numpages = {3}
}

@article{10.1007/s10791-016-9292-4,
author = {Liu, Zeyang and Mao, Jiaxin and Wang, Chao and Ai, Qingyao and Liu, Yiqun and Nie, Jian-Yun},
title = {Enhancing Click Models with Mouse Movement Information},
year = {2017},
issue_date = {February  2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-016-9292-4},
doi = {10.1007/s10791-016-9292-4},
abstract = {User interactions in Web search, in particular, clicks, provide valuable hints on document relevance; but the signals are very noisy. In order to better understand user click behaviors and to infer the implied relevance, various click models have been proposed, each relying on some hypotheses and involving different hidden events (e.g. examination).
 In almost all the existing click models, it is assumed that clicks are the only observable evidence and the examinations of documents are deduced from it. However, with an increasing number of embedded heterogeneous components (e.g. verticals) on Search Engine Result Pages, click information is not sufficient to draw a complete picture of process of user examination, especially in federated search scenario. In practice, we can also collect mouse movement information, which has proven to have a strong correlation with examination. In this paper, we propose to incorporate mouse movement information into existing click models to enhance the estimation of examination. The enhanced click models are shown to have a better ability to predict both user clicks and document relevance, than the original models. The collection of mouse movement information has been implemented in a commercial search engine, showing the feasibility of the approach in practice.
},
journal = {Inf. Retr.},
month = feb,
pages = {53–80},
numpages = {28},
keywords = {Federated search, Search engine, Click model, Mouse movement}
}

@article{10.1007/s10791-016-9289-z,
author = {Yu, Hai-Tao and Jatowt, Adam and Blanco, Roi and Joho, Hideo and Jose, Joemon M.},
title = {Decoding Multi-Click Search Behavior Based on Marginal Utility},
year = {2017},
issue_date = {February  2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-016-9289-z},
doi = {10.1007/s10791-016-9289-z},
abstract = {Query logs contain rich feedback information from users interacting with search engines. Therefore, various click models have been developed to interpret users' search behavior and to extract useful knowledge from query logs. However, most existing models are not designed to consider novelty bias in click behavior. The underlying hypothesis behind this paper is that given the previously clicked documents, a user tends to choose documents which provide novel relevant information to satisfy her information need, rather than redundant relevant information. Moreover, the prior click models have been mainly tested on frequently occurring queries, hence, leaving a large proportion of sparse queries uncovered. In this paper, we propose to predict users' click behavior from the perspective of utility theory (i.e., utility and marginal utility). In particular, as a complement to the examination hypothesis, we introduce a new hypothesis called marginal utility hypothesis to characterize the effect of novelty bias on users' click behavior by exploring the semantic divergence among documents in a result list. Moreover, to cope with sparse or unseen queries that have not been observed in the training set, we use a set of descriptive features to quantify the probability of a document being relevant and probability of a document providing marginally (novel) useful information. Finally, a series of experiments are conducted on a real-world data set to validate the effectiveness of the proposed methods. The experimental results verify the effectiveness of interpreting users' click behavior based on the marginal utility hypothesis, especially when query sessions contain sparse queries or unseen query-document pairs.},
journal = {Inf. Retr.},
month = feb,
pages = {25–52},
numpages = {28},
keywords = {Query session, Click model, Novelty bias, Marginal utility}
}

@article{10.1007/s10791-016-9287-1,
author = {Guo, Jiafeng and Zhu, Xiaofei and Lan, Yanyan and Cheng, Xueqi},
title = {Modeling Users' Search Sessions for High Utility Query Recommendation},
year = {2017},
issue_date = {February  2017},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {20},
number = {1},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-016-9287-1},
doi = {10.1007/s10791-016-9287-1},
abstract = {Query recommendation has long been considered a key feature of search engines, which can improve users' search experience by providing useful query suggestions for their search tasks. Most existing approaches on query recommendation aim to recommend relevant queries, i.e., alternative queries similar to a user's initial query. However, the ultimate goal of query recommendation is to assist users to reformulate queries so that they can accomplish their search task successfully and quickly. Only considering relevance in query recommendation is apparently not directly toward this goal. In this paper, we argue that it is more important to directly recommend queries with high utility, i.e., queries that can better satisfy users' information needs. For this purpose, we attempt to infer query utility from users' sequential search behaviors recorded in their search sessions. Specifically, we propose a dynamic Bayesian network, referred as Query Utility Model (QUM), to capture query utility by simultaneously modeling users' reformulation and click behaviors. We then recommend queries with high utility to help users better accomplish their search tasks. We empirically evaluated the performance of our approach on a publicly released query log by comparing with the state-of-the-art methods. The experimental results show that, by recommending high utility queries, our approach is far more effective in helping users find relevant search results and thus satisfying their information needs.},
journal = {Inf. Retr.},
month = feb,
pages = {4–24},
numpages = {21},
keywords = {Query recommendation, Query utility, Search behavior, Dynamic Bayesian network}
}

@article{10.1007/s10791-016-9288-0,
author = {Song, Ruihua and Wang, Dingquan and Nie, Jian-Yun and Wen, Ji-Rong and Yu, Yong},
title = {Enhancing Web Search with Queries of Equivalent Intents},
year = {2016},
issue_date = {December  2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-016-9288-0},
doi = {10.1007/s10791-016-9288-0},
abstract = {Users often issue all kinds of queries to look for the same target due to the intrinsic ambiguity and flexibility of natural languages. Some previous work clusters queries based on co-clicks; however, the intents of queries in one cluster are not that similar but roughly related. It is desirable to conduct automatic mining of queries with equivalent intents from a large scale search logs. In this paper, we take account of similarities between query strings. There are two issues associated with such similarities: it is too costly to compare any pair of queries in large scale search logs, and two queries with a similar formulation, such as "SVN" (Apache Subversion) and support vector machine (SVM), are not necessarily similar in their intents. To address these issues, we propose using the similarities of query strings above the co-click based clustering results. Our method improves precision over the co-click based clustering method (lifting precision from 0.37 to 0.62), and outperforms a commercial search engine's query alteration (lifting $$F_1$$F1 measure from 0.42 to 0.56). As an application, we consider web document retrieval. We aggregate similar queries' click-throughs with the query's click-throughs and evaluate them on a large scale dataset. Experimental results indicate that our proposed method significantly outperforms the baseline method of using a query's own click-throughs in all metrics.},
journal = {Inf. Retr.},
month = dec,
pages = {573–593},
numpages = {21},
keywords = {Mining similar queries, Query intent, Web search}
}

@article{10.1007/s10791-016-9284-4,
author = {Gunes, Ihsan and Polat, Huseyin},
title = {Detecting Shilling Attacks in Private Environments},
year = {2016},
issue_date = {December  2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {6},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-016-9284-4},
doi = {10.1007/s10791-016-9284-4},
abstract = {Privacy-preserving collaborative filtering algorithms are successful approaches. However, they are susceptible to shilling attacks. Recent research has increasingly focused on collaborative filtering to protect against both privacy and shilling attacks. Malicious users may add fake profiles to manipulate the output of privacy-preserving collaborative filtering systems, which reduces the accuracy of these systems. Thus, it is imperative to detect fake profiles for overall success. Many methods have been developed for detecting attack profiles to keep them outside of the system. However, these techniques have all been established for non-private collaborative filtering schemes. The detection of shilling attacks in privacy-preserving recommendation systems has not been deeply examined. In this study, we examine the detection of shilling attacks in privacy-preserving collaborative filtering systems. We utilize four attack-detection methods to filter out fake profiles produced by six well-known shilling attacks on perturbed data. We evaluate these detection methods with respect to their ability to identify bogus profiles. Real data-based experiments are performed. Empirical outcomes demonstrate that some of the detection methods are very successful at filtering out fake profiles in privacy-preserving collaborating filtering schemes.},
journal = {Inf. Retr.},
month = dec,
pages = {547–572},
numpages = {26},
keywords = {Detection, Privacy, Shilling attack, Collaborative filtering, Recommendation}
}

@article{10.1007/s10791-016-9286-2,
author = {Verberne, Suzan and Sappelli, Maya and Hiemstra, Djoerd and Kraaij, Wessel},
title = {Evaluation and Analysis of Term Scoring Methods for Term Extraction},
year = {2016},
issue_date = {October   2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-016-9286-2},
doi = {10.1007/s10791-016-9286-2},
abstract = {We evaluate five term scoring methods for automatic term extraction on four different types of text collections: personal document collections, news articles, scientific articles and medical discharge summaries. Each collection has its own use case: author profiling, boolean query term suggestion, personalized query suggestion and patient query expansion. The methods for term scoring that have been proposed in the literature were designed with a specific goal in mind. However, it is as yet unclear how these methods perform on collections with characteristics different than what they were designed for, and which method is the most suitable for a given (new) collection. In a series of experiments, we evaluate, compare and analyse the output of six term scoring methods for the collections at hand. We found that the most important factors in the success of a term scoring method are the size of the collection and the importance of multi-word terms in the domain. Larger collections lead to better terms; all methods are hindered by small collection sizes (below 1000 words). The most flexible method for the extraction of single-word and multi-word terms is pointwise Kullback---Leibler divergence for informativeness and phraseness. Overall, we have shown that extracting relevant terms using unsupervised term scoring methods is possible in diverse use cases, and that the methods are applicable in more contexts than their original design purpose.},
journal = {Inf. Retr.},
month = oct,
pages = {510–545},
numpages = {36},
keywords = {Query suggestion, Evaluation, Term scoring, Query expansion, Author profiling, Term extraction}
}

@article{10.1007/s10791-016-9285-3,
author = {B\"{o}hm, Thilo and Klas, Claus-Peter and Hemmje, Matthias},
title = {Towards a Probabilistic Model for Supporting Collaborative Information Access},
year = {2016},
issue_date = {October   2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-016-9285-3},
doi = {10.1007/s10791-016-9285-3},
abstract = {In information retrieval research, models and systems traditionally assume that a single person is querying and reviewing the results. However, several empirical studies of professional practice identified collaboration during IR as everyday work patterns in order to solve a shared information need and to benefit from the diverse expertise and experience of the team members. Moreover, most IR systems that are employed in professional work routines are designed for individual use and prototype collaborative systems are too limited to support use in todays work practice. To bridge this gap, this papers develops and formalizes a decision theoretic approach towards supporting a team of people that explicitly set out together to resolve a shared information need. We develop a formal cost model for collaborative IR that considers the trade-off between estimated relevance of a document as well as estimated document redundancy. From this cost model, we use a decision theoretic approach to derive the notion of activity suggestions, that is, a formal optimum criterion that describes optimum collaboration strategies in IR as the solution of an integer linear program. Those collaboration strategies are suggested to team members with the aim to facilitate the collaborative performance of information retrieval tasks. We demonstrate the application of our model by means of search result division in two collaborative search tasks. In the conducted experiments, we study the effects of different domain knowledge and resulting relevance assessments of team members in four different conditions. The gathered results indicate that our approach can improve the retrieval effectiveness of teams in recall-oriented tasks.},
journal = {Inf. Retr.},
month = oct,
pages = {487–509},
numpages = {23},
keywords = {Task division, Integer linear programming, Information retrieval, Decision theory, Probabilistic ranking, Collaboration}
}

@article{10.1007/s10791-016-9283-5,
author = {Bordino, Ilaria and Lalmas, Mounia and Mejova, Yelena and Laere, Olivier},
title = {Beyond Entities: Promoting Explorative Search with Bundles},
year = {2016},
issue_date = {October   2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {5},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-016-9283-5},
doi = {10.1007/s10791-016-9283-5},
abstract = {Search engines are increasingly going beyond the pure relevance of search results to entertain users with information items that are interesting and even surprising, albeit sometimes not fully related to their search intent. In this paper, we study this serendipitous search space in the context of entity search, which has recently emerged as a powerful paradigm for building semantically rich answers. Specifically, our work proposes to enhance an explorative search system that represents a large sample of Yahoo Answers as an entity network, with a result structuring that goes beyond ranked lists, using composite entity retrieval, which requires a bundling of the results. We propose and compare six bundling methods, which exploit topical categories, entity specializations, and sentiment, and go beyond simple entity clustering. Two large-scale crowd-sourced studies show that users find a bundled organization--especially based on the topical categories of the query entity--to be better at revealing the most useful results, as well as at organizing the results, helping to discover novel and interesting information, and promoting exploration. Finally, a third study of 30 simulated search tasks reveals the bundled search experience to be less frustrating and more rewarding, with more users willing to recommend it to others.},
journal = {Inf. Retr.},
month = oct,
pages = {447–486},
numpages = {40},
keywords = {Composite eetrieval, Entity search, Explorative aearch, Entity networks, Topical bundles, Bundles}
}

@article{10.1007/s10791-016-9282-6,
author = {Lu, Xiaolu and Moffat, Alistair and Culpepper, J. Shane},
title = {The Effect of Pooling and Evaluation Depth on IR Metrics},
year = {2016},
issue_date = {August    2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-016-9282-6},
doi = {10.1007/s10791-016-9282-6},
abstract = {Batch IR evaluations are usually performed in a framework that consists of a document collection, a set of queries, a set of relevance judgments, and one or more effectiveness metrics. A large number of evaluation metrics have been proposed, with two primary families having emerged: recall-based metrics, and utility-based metrics. In both families, the pragmatics of forming judgments mean that it is usual to evaluate the metric to some chosen depth such as $$k=20$$k=20 or $$k=100$$k=100, without necessarily fully considering the ramifications associated with that choice. Our aim is this paper is to explore the relative risks arising with fixed-depth evaluation in the two families, and document the complex interplay between metric evaluation depth and judgment pooling depth. Using a range of TREC resources including NewsWire data and the ClueWeb collection, we: (1) examine the implications of finite pooling on the subsequent usefulness of different test collections, including specifying options for truncated evaluation; and (2) determine the extent to which various metrics correlate with themselves when computed to different evaluation depths using those judgments. We demonstrate that the judgment pools constructed for the ClueWeb collections lack resilience, and are suited primarily to the application of top-heavy utility-based metrics rather than recall-based metrics; and that on the majority of the established test collections, and across a range of evaluation depths, recall-based metrics tend to be more volatile in the system rankings they generate than are utility-based metrics. That is, experimentation using utility-based metrics is more robust to choices such as the evaluation depth employed than is experimentation using recall-based metrics. This distinction should be noted by researchers as they plan and execute system-versus-system retrieval experiments.},
journal = {Inf. Retr.},
month = aug,
pages = {416–445},
numpages = {30},
keywords = {Evaluation metrics comparison, Pooling and evaluation depth, Experimentation}
}

@article{10.1007/s10791-016-9280-8,
author = {Manzato, Marcelo G. and Domingues, Marcos A. and Fortes, Arthur C. and Sundermann, Camila V. and D'addio, Rafael M. and Conrado, Merley S. and Rezende, Solange O. and Pimentel, Maria G.},
title = {Mining Unstructured Content for Recommender Systems: An Ensemble Approach},
year = {2016},
issue_date = {August    2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-016-9280-8},
doi = {10.1007/s10791-016-9280-8},
abstract = {Recommendation of textual documents requires indexing mechanisms to extract structured metadata for attribute-aware recommender systems. Applying a variety of text mining algorithms has the advantage of capturing different aspects of unstructured content, resulting in richer descriptions. However, it is difficult to integrate them into a unique model so that these descriptions can efficiently improve recommendation accuracy. This article proposes a generic model based on ensemble learning that combines simple text mining methods in a post-processing approach. After executing each text mining technique, each set of metadata of a particular type is applied to the recommender module, which generates attribute-specific rankings. Then, the resulting recommendations are ensembled to generate a final personalized ranking to the user. We evaluated our ensemble technique with two attribute-aware collaborative recommenders (k-Nearest Neighbors and BPR-Mapping) and we demonstrate its generality by means of comparisons among different types of ensembles. We used two datasets from different domains, the first is from the Brazilian Embrapa Agency of Technology Information website, whose documents are written in Portuguese language, and the second is the HetRec MovieLens 2k, published by the GroupLens Research Group, whose movies' storylines are written in English. The experiments show that, particularly to the k-NN recommender, better accuracy can be obtained when multiple metadata types are combined. The proposed approach is extensible and flexible to new indexing and recommendation techniques.},
journal = {Inf. Retr.},
month = aug,
pages = {378–415},
numpages = {38},
keywords = {Ensemble learning, Metadata awareness, Recommender systems, Personalized ranking, Unstructured content}
}

@article{10.1007/s10791-016-9279-1,
author = {Clarke, Charles L. and Culpepper, J. Shane and Moffat, Alistair},
title = {Assessing Efficiency---Effectiveness Tradeoffs in Multi-Stage Retrieval Systems without Using Relevance Judgments},
year = {2016},
issue_date = {August    2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {4},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-016-9279-1},
doi = {10.1007/s10791-016-9279-1},
abstract = {Large-scale retrieval systems are often implemented as a cascading sequence of phases--a first filtering step, in which a large set of candidate documents are extracted using a simple technique such as Boolean matching and/or static document scores; and then one or more ranking steps, in which the pool of documents retrieved by the filter is scored more precisely using dozens or perhaps hundreds of different features. The documents returned to the user are then taken from the head of the final ranked list. Here we examine methods for measuring the quality of filtering and preliminary ranking stages, and show how to use these measurements to tune the overall performance of the system. Standard top-weighted metrics used for overall system evaluation are not appropriate for assessing filtering stages, since the output is a set of documents, rather than an ordered sequence of documents. Instead, we use an approach in which a quality score is computed based on the discrepancy between filtered and full evaluation. Unlike previous approaches, our methods do not require relevance judgments, and thus can be used with virtually any query set. We show that this quality score directly correlates with actual differences in measured effectiveness when relevance judgments are available. Since the quality score does not require relevance judgments, it can be used to identify queries that perform particularly poorly for a given filter. Using these methods, we explore a wide range of filtering options using thousands of queries, categorize the relative merits of the different approaches, and identify useful parameter combinations.},
journal = {Inf. Retr.},
month = aug,
pages = {351–377},
numpages = {27},
keywords = {Experimentation, Effectiveness, Measurement, Search, Efficiency}
}

@article{10.1007/s10791-016-9281-7,
author = {Scholer, Falk and Kelly, Diane and Carterette, Ben},
title = {Information Retrieval Evaluation Using Test Collections},
year = {2016},
issue_date = {June      2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-016-9281-7},
doi = {10.1007/s10791-016-9281-7},
journal = {Inf. Retr.},
month = jun,
pages = {225–229},
numpages = {5}
}

@article{10.1007/s10791-015-9276-9,
author = {Samar, Thaer and Bellog\'{\i}n, Alejandro and Vries, Arjen P.},
title = {The Strange Case of Reproducibility versus Representativeness in Contextual Suggestion Test Collections},
year = {2016},
issue_date = {June      2016},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {19},
number = {3},
issn = {1386-4564},
url = {https://doi.org/10.1007/s10791-015-9276-9},
doi = {10.1007/s10791-015-9276-9},
abstract = {The most common approach to measuring the effectiveness of Information Retrieval systems is by using test collections. The Contextual Suggestion (CS) TREC track provides an evaluation framework for systems that recommend items to users given their geographical context. The specific nature of this track allows the participating teams to identify candidate documents either from the Open Web or from the ClueWeb12 collection, a static version of the web. In the judging pool, the documents from the Open Web and ClueWeb12 collection are distinguished. Hence, each system submission should be based only on one resource, either Open Web (identified by URLs) or ClueWeb12 (identified by ids). To achieve reproducibility, ranking web pages from ClueWeb12 should be the preferred method for scientific evaluation of CS systems, but it has been found that the systems that build their suggestion algorithms on top of input taken from the Open Web achieve consistently a higher effectiveness. Because most of the systems take a rather similar approach to making CSs, this raises the question whether systems built by researchers on top of

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repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr