text:
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098157},
doi = {10.1145/3097983.3098157},
abstract = {Efficient management and control of modern and next-gen networks is of paramount importance as networks have to maintain highly reliable service quality whilst supporting rapid growth in traffic demand and new application services. Rapid mitigation of network service degradations is a key factor in delivering high service quality. Automation is vital to achieving rapid mitigation of issues, particularly at the network edge where the scale and diversity is the greatest. This automation involves the rapid detection, localization and (where possible) repair of service-impacting faults and performance impairments. However, the most significant challenge here is knowing what events to detect, how to correlate events to localize an issue and what mitigation actions should be performed in response to the identified issues. These are defined as policies to systems such as ECOMP.In this paper, we present AESOP, a data-driven intelligent system to facilitate automatic learning of policies and rules for triggering remedial actions in networks. AESOP combines best operational practices (domain knowledge) with a variety of measurement data to learn and validate operational policies to mitigate service issues in networks. AESOP's design addresses the following key challenges: (i) learning from high-dimensional noisy data, (ii) capturing multiple fault models, (iii) modeling the high service-cost of false positives, and (iv) accounting for the evolving network infrastructure. We present the design of our system and show results from our ongoing experiments to show the effectiveness of our policy leaning framework.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1783–1792},
numpages = {10},
keywords = {policy learning, network management, supervised learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098193,
author = {Ghosh, Shalini and Das, Ariyam and Porras, Phil and Yegneswaran, Vinod and Gehani, Ashish},
title = {Automated Categorization of Onion Sites for Analyzing the Darkweb Ecosystem},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098193},
doi = {10.1145/3097983.3098193},
abstract = {Onion sites on the darkweb operate using the Tor Hidden Service (HS) protocol to shield their locations on the Internet, which (among other features) enables these sites to host malicious and illegal content while being resistant to legal action and seizure. Identifying and monitoring such illicit sites in the darkweb is of high relevance to the Computer Security and Law Enforcement communities. We have developed an automated infrastructure that crawls and indexes content from onion sites into a large-scale data repository, called LIGHTS, with over 100M pages. In this paper we describe Automated Tool for Onion Labeling (ATOL), a novel scalable analysis service developed to conduct a thematic assessment of the content of onion sites in the LIGHTS repository. ATOL has three core components -- (a) a novel keyword discovery mechanism (ATOLKeyword) which extends analyst-provided keywords for different categories by suggesting new descriptive and discriminative keywords that are relevant for the categories; (b) a classification framework (ATOLClassify) that uses the discovered keywords to map onion site content to a set of categories when sufficient labeled data is available; (c) a clustering framework (ATOLCluster) that can leverage information from multiple external heterogeneous knowledge sources, ranging from domain expertise to Bitcoin transaction data, to categorize onion content in the absence of sufficient supervised data. The paper presents empirical results of ATOL on onion datasets derived from the LIGHTS repository, and additionally benchmarks ATOL's algorithms on the publicly available 20 Newsgroups dataset to demonstrate the reproducibility of its results. On the LIGHTS dataset, ATOLClassify gives a 12% performance gain over an analyst-provided baseline, while ATOLCluster gives a 7% improvement over state-of-the-art semi-supervised clustering algorithms. We also discuss how ATOL has been deployed and externally evaluated, as part of the LIGHTS system.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1793–1802},
numpages = {10},
keywords = {clustering, classification, semi-supervised learning, onion sites, keyword discovery, darkweb},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098131,
author = {Hassan, Naeemul and Arslan, Fatma and Li, Chengkai and Tremayne, Mark},
title = {Toward Automated Fact-Checking: Detecting Check-Worthy Factual Claims by ClaimBuster},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098131},
doi = {10.1145/3097983.3098131},
abstract = {This paper introduces how ClaimBuster, a fact-checking platform, uses natural language processing and supervised learning to detect important factual claims in political discourses. The claim spotting model is built using a human-labeled dataset of check-worthy factual claims from the U.S. general election debate transcripts. The paper explains the architecture and the components of the system and the evaluation of the model. It presents a case study of how ClaimBuster live covers the 2016 U.S. presidential election debates and monitors social media and Australian Hansard for factual claims. It also describes the current status and the long-term goals of ClaimBuster as we keep developing and expanding it.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1803–1812},
numpages = {10},
keywords = {text classification, text mining, computational journalism, fact-checking, natural language processing},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098184,
author = {Hill, Daniel N. and Nassif, Houssam and Liu, Yi and Iyer, Anand and Vishwanathan, S.V.N.},
title = {An Efficient Bandit Algorithm for Realtime Multivariate Optimization},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098184},
doi = {10.1145/3097983.3098184},
abstract = {Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Often the layout of these pages can be decoupled into several separate decisions. For example, the composition of a landing page may involve deciding which image to show, which wording to use, what color background to display, etc. Such optimization is a combinatorial problem over an exponentially large decision space. Randomized experiments do not scale well to this setting, and therefore, in practice, one is typically limited to optimizing a single aspect of a web page at a time. This represents a missed opportunity in both the speed of experimentation and the exploitation of possible interactions between layout decisionsHere we focus on multivariate optimization of interactive web pages. We formulate an approach where the possible interactions between different components of the page are modeled explicitly. We apply bandit methodology to explore the layout space efficiently and use hill-climbing to select optimal content in realtime. Our algorithm also extends to contextualization and personalization of layout selection. Simulation results show the suitability of our approach to large decision spaces with strong interactions between content. We further apply our algorithm to optimize a message that promotes adoption of an Amazon service. After only a single week of online optimization, we saw a 21% conversion increase compared to the median layout. Our technique is currently being deployed to optimize content across several locations at Amazon.com.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1813–1821},
numpages = {9},
keywords = {multivariate optimization, hill-climbing, a/b testing, multi-armed bandit},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098159,
author = {Iosifidis, Vasileios and Ntoutsi, Eirini},
title = {Large Scale Sentiment Learning with Limited Labels},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098159},
doi = {10.1145/3097983.3098159},
abstract = {Sentiment analysis is an important task in order to gain insights over the huge amounts of opinions that are generated in the social media on a daily basis. Although there is a lot of work on sentiment analysis, there are no many datasets available which one can use for developing new methods and for evaluation. To the best of our knowledge, the largest dataset for sentiment analysis is TSentiment [8], a 1.6 millions machine-annotated tweets dataset covering a period of about 3 months in 2009. This dataset however is too short and therefore insufficient to study heterogeneous, fast evolving streams. Therefore, we annotated the Twitter dataset of 2015 (228 million tweets without retweets and 275 million with retweets) and we make it publicly available for research. For the annotation we leverage the power of unlabeled data, together with labeled data using semi-supervised learning and in particular, Self-Learning and Co-Training. Our main contribution is the provision of the TSentiment15 dataset together with insights from the analysis, which includes a batch and a stream-processing of the data. In the former, all labeled and unlabeled data are available to the algorithms from the beginning, whereas in the later, they are revealed gradually based on their arrival time in the stream.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1823–1832},
numpages = {10},
keywords = {sentiment analysis, semi-supervised learning, co-training, self-learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098188,
author = {Ito, Shinji and Fujimaki, Ryohei},
title = {Optimization Beyond Prediction: Prescriptive Price Optimization},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098188},
doi = {10.1145/3097983.3098188},
abstract = {This paper addresses a novel data science problem, prescriptive price optimization, which derives the optimal price strategy to maximize future profit/revenue on the basis of massive predictive formulas produced by machine learning. The prescriptive price optimization first builds sales forecast formulas of multiple products, on the basis of historical data, which reveal complex relationships between sales and prices, such as price elasticity of demand and cannibalization. Then, it constructs a mathematical optimization problem on the basis of those predictive formulas. We present that the optimization problem can be formulated as an instance of binary quadratic programming (BQP). Although BQP problems are NP-hard in general and computationally intractable, we propose a fast approximation algorithm using a semi-definite programming (SDP) relaxation. Our experiments on simulation and real retail datasets show that our prescriptive price optimization simultaneously derives the optimal prices of tens/hundreds products with practical computational time, that potentially improve approximately 30% of gross profit of those products.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1833–1841},
numpages = {9},
keywords = {scheduling, mathematical optimization, decision making},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098097,
author = {Janakiraman, Vijay Manikandan and Matthews, Bryan and Oza, Nikunj},
title = {Finding Precursors to Anomalous Drop in Airspeed During a Flight's Takeoff},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098097},
doi = {10.1145/3097983.3098097},
abstract = {Aerodynamic stall based loss of control in flight is a major cause of fatal flight accidents. In a typical takeoff, a flight's airspeed continues to increase as it gains altitude. However, in some cases, the airspeed may drop immediately after takeoff and when left uncorrected, the flight gets close to a stall condition which is extremely risky. The takeoff is a high workload period for the flight crew involving frequent monitoring, control and communication with the ground control tower. Although there exists secondary safety systems and specialized recovery maneuvers, current technology is reactive; often based on simple threshold detection and does not provide the crew with sufficient lead time. Further, with increasing complexity of automation, the crew may not be aware of the true states of the automation to take corrective actions in time. At NASA, we aim to develop decision support tools by mining historic flight data to proactively identify and manage high risk situations encountered in flight. In this paper, we present our work on finding precursors to the anomalous drop-in-airspeed (ADA) event using the ADOPT (Automatic Discovery of Precursors in Time series) algorithm. ADOPT works by converting the precursor discovery problem into a search for sub-optimal decision making in the time series data, which is modeled using reinforcement learning. We give insights about the flight data, feature selection, ADOPT modeling and results on precursor discovery. Some improvements to ADOPT algorithm are implemented that reduces its computational complexity and enables forecasting of the adverse event. Using ADOPT analysis, we have identified some interesting precursor patterns that were validated to be operationally significant by subject matter experts. The performance of ADOPT is evaluated by using the precursor scores as features to predict the drop in airspeed events.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1843–1852},
numpages = {10},
keywords = {phm, aircraft stall, aviation data mining, precursor, applications, decision support, aviation safety, reinforcement learning, vehicle health management, prognostics, time series, anomaly detection},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098085,
author = {Kitts, Brendan and Krishnan, Michael and Yadav, Ishadutta and Zeng, Yongbo and Badeau, Garrett and Potter, Andrew and Tolkachov, Sergey and Thornburg, Ethan and Janga, Satyanarayana Reddy},
title = {Ad Serving with Multiple KPIs},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098085},
doi = {10.1145/3097983.3098085},
abstract = {Ad-servers have to satisfy many different targeting criteria, and the combination can often result in no feasible solution. We hypothesize that advertisers may be defining these metrics to create a kind of "proxy target". We therefore reformulate the standard ad-serving problem to one where we attempt to get as close as possible to the advertiser's multi-dimensional target inclusive of delivery. We use a simple simulation to illustrate the behavior of this algorithm compared to Constraint and Pacing strategies. The system is then deployed in one of the largest video ad-servers in the United States and we show experimental results from live test ads, as well as 6 months of production performance across hundreds of ads. We find that the live ad-server tests match the simulation, and we report significant gains in multi-KPI performance from using the error minimization strategy.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1853–1861},
numpages = {9},
keywords = {viewability, internet advertising bureau, optimization, iab, targeting, advertising, prediction},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098090,
author = {Li, Xiucheng and Cheng, Yun and Cong, Gao and Chen, Lisi},
title = {Discovering Pollution Sources and Propagation Patterns in Urban Area},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098090},
doi = {10.1145/3097983.3098090},
abstract = {Air quality is one of the most important environmental concerns in the world, and it has deteriorated substantially over the past years in many countries. For example, Chinese Academy of Social Sciences reports that the problem of haze and fog in China is hitting a record level, and China is currently suffering from the worst air pollution. Among the various causal factors of air quality, particulate matter with a diameter of 2.5 micrometers or less (i.e., PM2.5) is a very important factor; governments and people are increasingly concerned with the concentration of PM2.5. In many cities, stations for monitoring PM2.5 concentration have been built by governments or companies to monitor urban air quality. Apart from monitoring, there is a rising demand for finding pollution sources of PM2.5 and discovering the transmission of PM2.5 based on the data from PM$_{2.5}$ monitoring stations.However, to the best of our knowledge, none of previous work proposes a solution to the problem of detecting pollution sources and mining pollution propagation patterns from such monitoring data. In this work, we propose the first solution for the problem, which comprises two steps. The first step is to extract the uptrend intervals and calculate the causal strengths among spatially distributed sensors; The second step is to construct causality graphs and perform frequent subgraphs mining on these causality graphs to find pollution sources and propagation patterns. We use real-life monitoring data collected by a company in our experiments. Our experimental results demonstrate significant findings regarding pollution sources and pollutant propagations in Beijing, which will be useful for governments to make policy and govern pollution sources.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1863–1872},
numpages = {10},
keywords = {propagation patterns, pollution sources, real deployed system},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098102,
author = {Li, Keqian and He, Yeye and Ganjam, Kris},
title = {Discovering Enterprise Concepts Using Spreadsheet Tables},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098102},
doi = {10.1145/3097983.3098102},
abstract = {Existing work on knowledge discovery focuses on using natural language techniques to extract entities and relationships from textual documents. However, today relational tables are abundant in quantities, and are often well-structured with coherent data values. So far these rich relational tables have been largely overlooked for the purpose of knowledge discovery. In this work, we study the problem of building concept hierarchies using a large corpus of enterprise spreadsheet tables. Our method first groups distinct values from tables into a large hierarchical tre based on co-occurrence statistics. We then "summarize" the large tree by selecting important tree nodes that are likely good concepts based on how well they "describe" the original corpus. The result is a small concept hierarchy that is easy for humans to understand and curate. Our end-to-end algorithms are designed to run on Map-Reduce and to scale to large corpus. Experiments using real enterprise spreadsheet corpus show that proposed approach can generate concepts with high quality.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1873–1882},
numpages = {10},
keywords = {hierarchical tree, clustering, enterprise concepts, knowledge discovery},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098093,
author = {Liu, Qiaoling and Javed, Faizan and Dave, Vachik S. and Joshi, Ankita},
title = {Supporting Employer Name Normalization at Both Entity and Cluster Level},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098093},
doi = {10.1145/3097983.3098093},
abstract = {In the recruitment domain, the employer name normalization task, which links employer names in job postings or resumes to entities in an employer knowledge base (KB), is important to many business applications. In previous work, we proposed the CompanyDepot system, which used machine learning techniques to address the problem. After applying it to several applications at CareerBuilder, we faced several new challenges: 1) how to avoid duplicate normalization results when the KB is noisy and contains many duplicate entities; 2) how to address the vocabulary gap between query names and entity names in the KB; and 3) how to use the context available in jobs and resumes to improve normalization quality. To address these challenges, in this paper we extend the previous CompanyDepot system to normalize employer names not only at entity level, but also at cluster level by mapping a query to a cluster in the KB that best matches the query. We also propose a new metric based on success rate and diversity reduction ratio for evaluating the cluster-level normalization. Moreover, we perform query expansion based on five data sources to address the vocabulary gap challenge and leverage the url context for the employer names in many jobs and resumes to improve normalization quality. We show that the proposed CompanyDepot-V2 system outperforms the previous CompanyDepot system and several other baseline systems over multiple real-world datasets. We also demonstrate the large improvement on normalization quality from entity-level to cluster-level normalization.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1883–1892},
numpages = {10},
keywords = {success rate, employer name normalization, entity linking, cluster-level normalization, diversity reduction ratio},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098175,
author = {Lou, Yin and Obukhov, Mikhail},
title = {BDT: Gradient Boosted Decision Tables for High Accuracy and Scoring Efficiency},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098175},
doi = {10.1145/3097983.3098175},
abstract = {In this paper we present gradient boosted decision tables (BDTs). A d-dimensional decision table is essentially a mapping from a sequence of d boolean tests to a real value in {R}. We propose novel algorithms to fit decision tables. Our thorough empirical study suggests that decision tables are better weak learners in the gradient boosting framework and can improve the accuracy of the boosted ensemble. In addition, we develop an efficient data structure to represent decision tables and propose a novel fast algorithm to improve the scoring efficiency for boosted ensemble of decision tables. Experiments on public classification and regression datasets demonstrate that our method is able to achieve 1.5x to 6x speedups over the boosted regression trees baseline. We complement our experimental evaluation with a bias-variance analysis that explains how different weak models influence the predictive power of the boosted ensemble. Our experiments suggest gradient boosting with randomly backfitted decision tables distinguishes itself as the most accurate method on a number of classification and regression problems. We have deployed a BDT model to LinkedIn news feed system and achieved significant lift on key metrics.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1893–1901},
numpages = {9},
keywords = {regression, decision table, gradient boosting, classification},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098088,
author = {Ma, Fenglong and Chitta, Radha and Zhou, Jing and You, Quanzeng and Sun, Tong and Gao, Jing},
title = {Dipole: Diagnosis Prediction in Healthcare via Attention-Based Bidirectional Recurrent Neural Networks},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098088},
doi = {10.1145/3097983.3098088},
abstract = {Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and procedure codes. The most important challenges for this task are to model the temporality and high dimensionality of sequential EHR data and to interpret the prediction results. Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results. However, RNN-based approaches suffer from the problem that the performance of RNNs drops when the length of sequences is large, and the relationships between subsequent visits are ignored by current RNN-based approaches. To address these issues, we propose Dipole, an end-to-end, simple and robust model for predicting patients' future health information. Dipole employs bidirectional recurrent neural networks to remember all the information of both the past visits and the future visits, and it introduces three attention mechanisms to measure the relationships of different visits for the prediction. With the attention mechanisms, Dipole can interpret the prediction results effectively. Dipole also allows us to interpret the learned medical code representations which are confirmed positively by medical experts. Experimental results on two real world EHR datasets show that the proposed Dipole can significantly improve the prediction accuracy compared with the state-of-the-art diagnosis prediction approaches and provide clinically meaningful interpretation.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1903–1911},
numpages = {9},
keywords = {attention mechanism, bidirectional recurrent neural networks, healthcare informatics},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098114,
author = {Malloy, Matthew and Barford, Paul and Alp, Enis Ceyhun and Koller, Jonathan and Jewell, Adria},
title = {Internet Device Graphs},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098114},
doi = {10.1145/3097983.3098114},
abstract = {Internet device graphs identify relationships between user-centric internet connected devices such as desktops, laptops, smartphones, tablets, gaming consoles, TV's, etc. The ability to create such graphs is compelling for online advertising, content customization, recommendation systems, security, and operations. We begin by describing an algorithm for generating a device graph based on IP-colocation, and then apply the algorithm to a corpus of over 2.5 trillion internet events collected over the period of six weeks in the United States. The resulting graph exhibits immense scale with greater than 7.3 billion edges (pair-wise relationships) between more than 1.2 billion nodes (devices), accounting for the vast majority of internet connected devices in the US. Next, we apply community detection algorithms to the graph resulting in a partitioning of internet devices into 100 million small communities representing physical households. We validate this partition with a unique ground truth dataset. We report on the characteristics of the graph and the communities. Lastly, we discuss the important issues of ethics and privacy that must be considered when creating and studying device graphs, and suggest further opportunities for device graph enrichment and application.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1913–1921},
numpages = {9},
keywords = {internet device graphs},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098104,
author = {Manzoor, Emaad and Akoglu, Leman},
title = {RUSH! Targeted Time-Limited Coupons via Purchase Forecasts},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098104},
doi = {10.1145/3097983.3098104},
abstract = {Time-limited promotions that exploit consumers' sense of urgency to boost sales account for billions of dollars in consumer spending each year. However, it is challenging to discover the right timing and duration of a promotion to increase its chances of being redeemed. In this work, we consider the problem of delivering time-limited discount coupons, where we partner with a large national bank functioning as a commission-based third-party coupon provider. Specifically, we use large-scale anonymized transaction records to model consumer spending and forecast future purchases, based on which we generate data-driven, personalized coupons. Our proposed model RUSH! (1) predicts {both the time and category} of the next event; (2) captures correlations between purchases in different categories (such as shopping triggering dining purchases); (3) incorporates temporal dynamics of purchase behavior (such as increased spending on weekends); (4) is composed of additive factors that are easily interpretable; and finally (5) scales linearly to millions of transactions. We design a cost-benefit framework that facilitates systematic evaluation in terms of our application, and show that RUSH! provides higher expected value than various baselines that do not jointly model time and category information.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1923–1931},
numpages = {9},
keywords = {transaction data, targeted promotions and discounts, temporal point processes, cost-benefit analysis},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098108,
author = {Okura, Shumpei and Tagami, Yukihiro and Ono, Shingo and Tajima, Akira},
title = {Embedding-Based News Recommendation for Millions of Users},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098108},
doi = {10.1145/3097983.3098108},
abstract = {It is necessary to understand the content of articles and user preferences to make effective news recommendations. While ID-based methods, such as collaborative filtering and low-rank factorization, are well known for making recommendations, they are not suitable for news recommendations because candidate articles expire quickly and are replaced with new ones within short spans of time. Word-based methods, which are often used in information retrieval settings, are good candidates in terms of system performance but have issues such as their ability to cope with synonyms and orthographical variants and define "queries" from users' historical activities. This paper proposes an embedding-based method to use distributed representations in a three step end-to-end manner: (i) start with distributed representations of articles based on a variant of a denoising autoencoder, (ii) generate user representations by using a recurrent neural network (RNN) with browsing histories as input sequences, and (iii) match and list articles for users based on inner-product operations by taking system performance into consideration. The proposed method performed well in an experimental offline evaluation using past access data on Yahoo! JAPAN's homepage. We implemented it on our actual news distribution system based on these experimental results and compared its online performance with a method that was conventionally incorporated into the system. As a result, the click-through rate (CTR) improved by 23% and the total duration improved by 10%, compared with the conventionally incorporated method. Services that incorporated the method we propose are already open to all users and provide recommendations to over ten million individual users per day who make billions of accesses per month.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1933–1942},
numpages = {10},
keywords = {large-scale services, neural networks, distributed representations, news recommendations},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098204,
author = {Ovadia, Yaniv and Halpern, Yoni and Krishnan, Dilip and Livni, Josh and Newburger, Daniel and Poplin, Ryan and Zha, Tiantian and Sculley, D.},
title = {Learning to Count Mosquitoes for the Sterile Insect Technique},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098204},
doi = {10.1145/3097983.3098204},
abstract = {Mosquito-borne illnesses such as dengue, chikungunya, and Zika are major global health problems, which are not yet addressable with vaccines and must be countered by reducing mosquito populations. The Sterile Insect Technique (SIT) is a promising alternative to pesticides; however, effective SIT relies on minimal releases of female insects. This paper describes a multi-objective convolutional neural net to significantly streamline the process of counting male and female mosquitoes released from a SIT factory and provides a statistical basis for verifying strict contamination rate limits from these counts despite measurement noise. These results are a promising indication that such methods may dramatically reduce the cost of effective SIT methods in practice.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1943–1949},
numpages = {7},
keywords = {counting from images, quality assurance, image modeling},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098120,
author = {Pan, Lujia and Zhang, Jianfeng and Lee, Patrick P.C. and Cheng, Hong and He, Cheng and He, Caifeng and Zhang, Keli},
title = {An Intelligent Customer Care Assistant System for Large-Scale Cellular Network Diagnosis},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098120},
doi = {10.1145/3097983.3098120},
abstract = {With the advent of cellular network technologies, mobile Internet access becomes the norm in everyday life. In the meantime, the complaints made by subscribers about unsatisfactory cellular network access also become increasingly frequent. From a network operator's perspective, achieving accurate and timely cellular network diagnosis about the causes of the complaints is critical for both improving subscriber-perceived experience and maintaining network robustness. We present the Intelligent Customer Care Assistant (ICCA), a distributed fault classification system that exploits a data-driven approach to perform large-scale cellular network diagnosis. ICCA takes massive network data as input, and realizes both offline model training and online feature computation to distinguish between user and network faults in real time. ICCA is currently deployed in a metropolitan LTE network in China that is serving around 50 million subscribers. We show via evaluation that ICCA achieves high classification accuracy (85.3%) and fast query response time (less than 2.3 seconds). We also report our experiences learned from the deployment.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1951–1959},
numpages = {9},
keywords = {fault classification, cellular network diagnosis, sequential pattern mining},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098176,
author = {Pan, Yanxin and Burnap, Alexander and Hartley, Jeffrey and Gonzalez, Richard and Papalambros, Panos Y.},
title = {Deep Design: Product Aesthetics for Heterogeneous Markets},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098176},
doi = {10.1145/3097983.3098176},
abstract = {Aesthetic appeal is a primary driver of customer consideration for products such as automobiles. Product designers must accordingly convey design attributes (e.g., 'Sportiness'), a challenging proposition given the subjective nature of aesthetics and heterogeneous market segments with potentially different aesthetic preferences. We introduce a scalable deep learning approach that predicts how customers across different market segments perceive aesthetic designs and provides a visualization that can aid in product design. We tested this approach using a large-scale product design and crowdsourced customer data set with a Siamese neural network architecture containing a pair of conditional generative adversarial networks. The results show that the model predicts aesthetic design attributes of customers in heterogeneous market segments and provides a visualization of these aesthetic perceptions. This suggests that the proposed deep learning approach provides a scalable method for understanding customer aesthetic perceptions.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1961–1970},
numpages = {10},
keywords = {automobile design, heterogeneous markets, crowdsourcing, product aesthetics, deep learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098201,
author = {Quisel, Tom and Foschini, Luca and Signorini, Alessio and Kale, David C.},
title = {Collecting and Analyzing Millions of MHealth Data Streams},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098201},
doi = {10.1145/3097983.3098201},
abstract = {Players across the health ecosystem are initiating studies of thousands, even millions, of participants to gather diverse types of data, including biomedical, behavioral, and lifestyle in order to advance medical research. These efforts to collect multi-modal data sets on large cohorts coincide with the rise of broad activity and behavior tracking across industries, particularly in healthcare and the growing field of mobile health (mHealth). Government and pharmaceutical sponsored, as well as patient-driven group studies in this arena leverage the ability of mobile technology to continuously track behaviors and environmental factors with minimal participant burden. However, the adoption of mHealth has been constrained by the lack of robust solutions for large-scale data collection in free-living conditions and concerns around data quality. In this work, we describe the infrastructure Evidation Health has developed to collect mHealth data from millions of users through hundreds of different mobile devices and apps. Additionally, we provide evidence of the utility of the data for inferring individual traits pertaining to health, wellness, and behavior. To this end, we introduce and evaluate deep neural network models that achieve high prediction performance without requiring any feature engineering when trained directly on the densely sampled multivariate mHealth time series data. We believe that the present work substantiates both the feasibility and the utility of creating a very large mHealth research cohort, as envisioned by the many large cohort studies currently underway across therapeutic areas and conditions.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1971–1980},
numpages = {10},
keywords = {neural networks, mhealth, time series, wearables},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098178,
author = {Ristovski, Kosta and Gupta, Chetan and Harada, Kunihiko and Tang, Hsiu-Khuern},
title = {Dispatch with Confidence: Integration of Machine Learning, Optimization and Simulation for Open Pit Mines},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098178},
doi = {10.1145/3097983.3098178},
abstract = {Open pit mining operations require utilization of extremely expensive equipment such as large trucks, shovels and loaders. To remain competitive, mining companies are under pressure to increase equipment utilization and reduce operational costs. The key to this in mining operations is to have sophisticated truck assignment strategies which will ensure that equipment is utilized efficiently with minimum operating cost. To address this problem, we have implemented truck assignment approach which integrates machine learning, linear/integer programming and simulation. Our truck assignment approach takes into consideration the number of trucks and their sizes, shovels and dump locations as well as stochastic activity times during the operations. Machine learning is used to predict probability distributions of equipment activity duration. We have validated the approach using data collected from two open pit mines. Our experimental results show that our approach offers increase of 10% in efficiency. Presented results demonstrate that machine learning can bring significant value to mining industry.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1981–1989},
numpages = {9},
keywords = {machine learning, mine simulator, optimization},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098121,
author = {Ruiz, Hector and Power, Paul and Wei, Xinyu and Lucey, Patrick},
title = {"The Leicester City Fairytale?": Utilizing New Soccer Analytics Tools to Compare Performance in the 15/16 & 16/17 EPL Seasons},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098121},
doi = {10.1145/3097983.3098121},
abstract = {The last two years have been somewhat of a rollercoaster for English Premier League (EPL) team Leicester City. In the 2015/16 season, against all odds and logic, they won the league to much fan-fare. Fast-forward nine months later, and they are battling relegation. What could describe this fluctuating form? As soccer is a very complex and strategic game, common statistics (e.g., passes, shots, possession) do not really tell the full story on how a team succeeds and fails. However, using machine learning tools and a plethora of data, it is now possible to obtain some insights into how a team performs. To showcase the utility of these new tools (i.e., expected goal value, expected save value, strategy-plots and passing quality measures), we first analyze the EPL 2015/16 season which a specific emphasis on the champions Leicester City, and then compare it to the current one. Finally, we show how these features can be used to predict future performance.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1991–2000},
numpages = {10},
keywords = {prediction, sports analytics, unstructured data},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098125,
author = {Salehian, Hesam and Howell, Patrick and Lee, Chul},
title = {Matching Restaurant Menus to Crowdsourced Food Data: A Scalable Machine Learning Approach},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098125},
doi = {10.1145/3097983.3098125},
abstract = {We study the problem of how to match a formally structured restaurant menu item to a large database of less structured food items that has been collected via crowd-sourcing. At first glance, this problem scenario looks like a typical text matching problem that might possibly be solved with existing text similarity learning approaches. However, due to the unique nature of our scenario and the need for scalability, our problem imposes certain restrictions on possible machine learning approaches that we can employ. We propose a novel, practical, and scalable machine learning solution architecture, consisting of two major steps. First we use a query generation approach, based on a Markov Decision Process algorithm, to reduce the time complexity of searching for matching candidates. That is then followed by a re-ranking step, using deep learning techniques, to meet our required matching quality goals. It is important to note that our proposed solution architecture has already been deployed in a real application system serving tens of millions of users, and shows great potential for practical cases of user-entered text to structured text matching, especially when scalability is crucial.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2001–2009},
numpages = {9},
keywords = {convolutional neural networks, short text matching, markov decision process, nutrition estimation},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098186,
author = {Sharma, Ashlesh and Srinivasan, Vidyuth and Kanchan, Vishal and Subramanian, Lakshminarayanan},
title = {The Fake vs Real Goods Problem: Microscopy and Machine Learning to the Rescue},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098186},
doi = {10.1145/3097983.3098186},
abstract = {Counterfeiting of physical goods is a global problem amounting to nearly 7% of world trade. While there have been a variety of overt technologies like holograms and specialized barcodes and covert technologies like taggants and PUFs, these solutions have had a limited impact on the counterfeit market due to a variety of factors - clonability, cost or adoption barriers. In this paper, we introduce a new mechanism that uses machine learning algorithms on microscopic images of physical objects to distinguish between genuine and counterfeit versions of the same product. The underlying principle of our system stems from the idea that microscopic characteristics in a genuine product or a class of products (corresponding to the same larger product line), exhibit inherent similarities that can be used to distinguish these products from their corresponding counterfeit versions. A key building block for our system is a wide-angle microscopy device compatible with a mobile device that enables a user to easily capture the microscopic image of a large area of a physical object. Based on the captured microscopic images, we show that using machine learning algorithms (ConvNets and bag of words), one can generate a highly accurate classification engine for separating the genuine versions of a product from the counterfeit ones; this property also holds for "super-fake" counterfeits observed in the marketplace that are not easily discernible from the human eye. We describe the design of an end-to-end physical authentication system leveraging mobile devices, portable hardware and a cloud-based object verification ecosystem. We evaluate our system using a large dataset of 3 million images across various objects and materials such as fabrics, leather, pills, electronics, toys and shoes. The classification accuracy is more than 98% and we show how our system works with a cellphone to verify the authenticity of everyday objects.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2011–2019},
numpages = {9},
keywords = {conventional neural networks, computer vision, physical authentication, microscopy},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098196,
author = {Soska, Kyle and Gates, Chris and Roundy, Kevin A. and Christin, Nicolas},
title = {Automatic Application Identification from Billions of Files},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098196},
doi = {10.1145/3097983.3098196},
abstract = {Understanding how to group a set of binary files into the piece of software they belong to is highly desirable for software profiling, malware detection, or enterprise audits, among many other applications. Unfortunately, it is also extremely challenging: there is absolutely no uniformity in the ways different applications rely on different files, in how binaries are signed, or in the versioning schemes used across different pieces of software. In this paper, we show that, by combining information gleaned from a large number of endpoints (millions of computers), we can accomplish large-scale application identification automatically and reliably. Our approach relies on collecting metadata on billions of files every day, summarizing it into much smaller "sketches", and performing approximate k-nearest neighbor clustering on non-metric space representations derived from these sketches. We design and implement our proposed system using Apache Spark, show that it can process billions of files in a matter of hours, and thus could be used for daily processing. We further show our system manages to successfully identify which files belong to which application with very high precision, and adequate recall.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2021–2030},
numpages = {10},
keywords = {security, sketch, malware, files, aknn, clustering, knn, hsnw, big data, application},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098132,
author = {Tong, Bin and Klinkigt, Martin and Iwayama, Makoto and Yanase, Toshihiko and Kobayashi, Yoshiyuki and Sahu, Anshuman and Vennelakanti, Ravigopal},
title = {Learning to Generate Rock Descriptions from Multivariate Well Logs with Hierarchical Attention},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098132},
doi = {10.1145/3097983.3098132},
abstract = {In the shale oil & gas industry, operators are looking toward big data analytics to optimize operations and reduce cost. In this paper, we mainly focus on how to assist operators in understanding the subsurface formation, thereby helping them make optimal decisions. A large number of geology reports and well logs describing the sub-surface have been accumulated over years. Issuing geology reports is more time consuming and depends more on the expertise of engineers than acquiring the well logs. To assist in issuing geology reports, we propose an encoder-decoder-based model to automatically generate rock descriptions in human-readable format from multivariate well logs. Due to the different formats of data, this task differs dramatically from image and video captioning. The challenges are how to model structured rock descriptions and leverage the information in multivariate well logs. To achieve this, we design a hierarchical structure and two forms of attention for the decoder. Extensive validations are conducted on public well data of North Dakota in the United States. We show that our model is effective in generating rock descriptions. The two forms of attention enable the provision of a better insight into relations between well-log types and rock properties with our model from a data-driven perspective.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2031–2040},
numpages = {10},
keywords = {neural generation, geology report, attention, well log},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098194,
author = {Uesaka, Toshimitsu and Morino, Kai and Sugiura, Hiroki and Kiwaki, Taichi and Murata, Hiroshi and Asaoka, Ryo and Yamanishi, Kenji},
title = {Multi-View Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098194},
doi = {10.1145/3097983.3098194},
abstract = {Dense measurements of visual-field, which is necessary to detect glaucoma, is known as very costly and labor intensive. Recently, measurement of retinal-thickness can be less costly than measurement of visual-field. Thus, it is sincerely desired that the retinal-thickness could be transformed into visual-sensitivity data somehow. In this paper, we propose two novel methods to estimate the sensitivity of the visual-field with SITA-Standard mode 10-2 resolution using retinal-thickness data measured with optical coherence tomography (OCT). The first method called Affine-Structured Non-negative Matrix Factorization (ASNMF) which is able to cope with both the estimation of visual-field and the discovery of deep glaucoma knowledge. While, the second is based on Convolutional Neural Networks (CNNs) which demonstrates very high estimation performance. These methods are kinds of multi-view learning methods because they utilize visual-field and retinal thickness data simultaneously. We experimentally tested the performance of our methods from several perspectives. We found that ASNMF worked better for relatively small data size while CNNs did for relatively large data size. In addition, some clinical knowledge are discovered via ASNMF. To the best of our knowledge, this is the first paper to address the dense estimation of the visual-field based on the retinal-thickness data.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2041–2050},
numpages = {10},
keywords = {convolutional neural networks (cnns), glaucoma, non-negative matrix factorization (nmf), retinal thickness},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098096,
author = {Wang, Xuejian and Yu, Lantao and Ren, Kan and Tao, Guanyu and Zhang, Weinan and Yu, Yong and Wang, Jun},
title = {Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098096},
doi = {10.1145/3097983.3098096},
abstract = {As aggregators, online news portals face great challenges in continuously selecting a pool of candidate articles to be shown to their users. Typically, those candidate articles are recommended manually by platform editors from a much larger pool of articles aggregated from multiple sources. Such a hand-pick process is labor intensive and time-consuming. In this paper, we study the editor article selection behavior and propose a learning by demonstration system to automatically select a subset of articles from the large pool. Our data analysis shows that (i) editors' selection criteria are non-explicit, which are less based only on the keywords or topics, but more depend on the quality and attractiveness of the writing from the candidate article, which is hard to capture based on traditional bag-of-words article representation. And (ii) editors' article selection behaviors are dynamic: articles with different data distribution come into the pool everyday and the editors' preference varies, which are driven by some underlying periodic or occasional patterns. To address such problems, we propose a meta-attention model across multiple deep neural nets to (i) automatically catch the editors' underlying selection criteria via the automatic representation learning of each article and its interaction with the meta data and (ii) adaptively capture the change of such criteria via a hybrid attention model. The attention model strategically incorporates multiple prediction models, which are trained in previous days. The system has been deployed in a commercial article feed platform. A 9-day A/B testing has demonstrated the consistent superiority of our proposed model over several strong baselines.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2051–2059},
numpages = {9},
keywords = {recommendation, convolutional neural network, learning by demonstration, attention models},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098140,
author = {Wang, Chenglong and Jiang, Feijun and Yang, Hongxia},
title = {A Hybrid Framework for Text Modeling with Convolutional RNN},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098140},
doi = {10.1145/3097983.3098140},
abstract = {In this paper, we introduce a generic inference hybrid framework for Convolutional Recurrent Neural Network (conv-RNN) of semantic modeling of text, seamless integrating the merits on extracting different aspects of linguistic information from both convolutional and recurrent neural network structures and thus strengthening the semantic understanding power of the new framework. Besides, based on conv-RNN, we also propose a novel sentence classification model and an attention based answer selection model with strengthening power for the sentence matching and classification respectively. We validate the proposed models on a very wide variety of data sets, including two challenging tasks of answer selection (AS) and five benchmark datasets for sentence classification (SC). To the best of our knowledge, it is by far the most complete comparison results in both AS and SC. We empirically show superior performances of conv-RNN in these different challenging tasks and benchmark datasets and also summarize insights on the performances of other state-of-the-arts methodologies.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2061–2069},
numpages = {9},
keywords = {recurrent neural network, answer selection, sentence classification, convolution neural network, text modeling, hybrid framework},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098160,
author = {Woods, Bronwyn and Adamson, David and Miel, Shayne and Mayfield, Elijah},
title = {Formative Essay Feedback Using Predictive Scoring Models},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098160},
doi = {10.1145/3097983.3098160},
abstract = {A major component of secondary education is learning to write effectively, a skill which is bolstered by repeated practice with formative guidance. However, providing focused feedback to every student on multiple drafts of each essay throughout the school year is a challenge for even the most dedicated of teachers. This paper first establishes a new ordinal essay scoring model and its state of the art performance compared to recent results in the Automated Essay Scoring field. Extending this model, we describe a method for using prediction on realistic essay variants to give rubric-specific formative feedback to writers. This method is used in Revision Assistant, a deployed data-driven educational product that provides immediate, rubric-specific, sentence-level feedback to students to supplement teacher guidance. We present initial evaluations of this feedback generation, both offline and in deployment.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2071–2080},
numpages = {10},
keywords = {automated essay scoring, automated writing feedback, automated writing evaluation},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098100,
author = {Xiao, Houping and Gao, Jing and Vu, Long and Turaga, Deepak S.},
title = {Learning Temporal State of Diabetes Patients via Combining Behavioral and Demographic Data},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098100},
doi = {10.1145/3097983.3098100},
abstract = {Diabetes is a serious disease affecting a large number of people. Although there is no cure for diabetes, it can be managed. Especially, with advances in sensor technology, lots of data may lead to the improvement of diabetes management, if properly mined. However, there usually exists noise or errors in the observed behavioral data which poses challenges in extracting meaningful knowledge. To overcome this challenge, we learn the latent state which represents the patient's condition. Such states should be inferred from the behavioral data but unknown a priori. In this paper, we propose a novel framework to capture the trajectory of latent states for patients from behavioral data while exploiting their demographic differences and similarities to other patients. We conduct a hypothesis test to illustrate the importance of the demographic data in diabetes management, and validate that each behavioral feature follows an exponential or a Gaussian distribution. Integrating these aspects, we use a Demographic feature restricted hidden Markov model (DfrHMM) to estimate the trajectory of latent states by integrating the demographic and behavioral data. In DfrHMM, the latent state is mainly determined by the previous state and the demographic features in a nonlinear way. Markov Chain Monte Carlo techniques are used for model parameter estimation. Experiments on synthetic and real datasets show that DfrHMM is effective in diabetes management.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2081–2089},
numpages = {9},
keywords = {hidden markov model, diabetes management, hypothesis testing},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098089,
author = {Yang, Hongxia and Zhu, Yada and He, Jingrui},
title = {Local Algorithm for User Action Prediction Towards Display Ads},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098089},
doi = {10.1145/3097983.3098089},
abstract = {User behavior modeling is essential in computational advertisement, which builds users' profiles by tracking their online behaviors and then delivers the relevant ads according to each user's interests and needs. Accurate models will lead to higher targeting accuracy and thus improved advertising performance. Intuitively, similar users tend to have similar behaviors towards the displayed ads (e.g., impression, click, conversion). However, to the best of our knowledge, there is not much previous work that explicitly investigates such similarities of various types of user behaviors, and incorporates them into ad response targeting and prediction, largely due to the prohibitive scale of the problem.To bridge this gap, in this paper, we use bipartite graphs to represent historical user behaviors, which consist of both user nodes and advertiser campaign nodes, as well as edges reflecting various types of user-campaign interactions in the past. Based on this representation, we study random-walk-based local algorithms for user behavior modeling and action prediction, whose computational complexity depends only on the size of the output cluster, rather than the entire graph. Our goal is to improve action prediction by leveraging historical user-user, campaign-campaign, and user-campaign interactions. In particular, we propose the bipartite graphs AdvUserGraph accompanied with the ADNI algorithm. ADNI extends the NIBBLE algorithm to AdvUserGraph, and it is able to find the local cluster consisting of interested users towards a specific advertiser campaign. We also propose two extensions of ADNI with improved efficiencies. The performance of the proposed algorithms is demonstrated on both synthetic data and a world leading Demand Side Platform (DSP), showing that they are able to discriminate extremely rare events in terms of their action propensity.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2091–2099},
numpages = {9},
keywords = {user action prediction, computational advertisement, large scale, local graph algorithm},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098162,
author = {Yang, Fan and Kale, Ajinkya and Bubnov, Yury and Stein, Leon and Wang, Qiaosong and Kiapour, Hadi and Piramuthu, Robinson},
title = {Visual Search at EBay},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098162},
doi = {10.1145/3097983.3098162},
abstract = {In this paper, we propose a novel end-to-end approach for scalable visual search infrastructure. We discuss the challenges we faced for a massive volatile inventory like at eBay and present our solution to overcome those. We harness the availability of large image collection of eBay listings and state-of-the-art deep learning techniques to perform visual search at scale. Supervised approach for optimized search limited to top predicted categories and also for compact binary signature are key to scale up without compromising accuracy and precision. Both use a common deep neural network requiring only a single forward inference. The system architecture is presented with in-depth discussions of its basic components and optimizations for a trade-off between search relevance and latency. This solution is currently deployed in a distributed cloud infrastructure and fuels visual search in eBay ShopBot and Close5. We show benchmark on ImageNet dataset on which our approach is faster and more accurate than several unsupervised baselines. We share our learnings with the hope that visual search becomes a first class citizen for all large scale search engines rather than an afterthought.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2101–2110},
numpages = {10},
keywords = {e-commerce, search engine, semantics, visual search, deep learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098174,
author = {Yang, Sen and Dong, Xin and Sun, Leilei and Zhou, Yichen and Farneth, Richard A. and Xiong, Hui and Burd, Randall S. and Marsic, Ivan},
title = {A Data-Driven Process Recommender Framework},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098174},
doi = {10.1145/3097983.3098174},
abstract = {We present an approach for improving the performance of complex knowledge-based processes by providing data-driven step-by-step recommendations. Our framework uses the associations between similar historic process performances and contextual information to determine the prototypical way of enacting the process. We introduce a novel similarity metric for grouping traces into clusters that incorporates temporal information about activity performance and handles concurrent activities. Our data-driven recommender system selects the appropriate prototype performance of the process based on user-provided context attributes. Our approach for determining the prototypes discovers the commonly performed activities and their temporal relationships. We tested our system on data from three real-world medical processes and achieved recommendation accuracy up to an F1 score of 0.77 (compared to an F1 score of 0.37 using ZeroR) with 63.2% of recommended enactments being within the first five neighbors of the actual historic enactments in a set of 87 cases. Our framework works as an interactive visual analytic tool for process mining. This work shows the feasibility of data-driven decision support system for complex knowledge-based processes.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2111–2120},
numpages = {10},
keywords = {emergency medical process analysis., process trace clustering, process recommender system, process prototype extraction},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098198,
author = {Yilmaz, Emre and Elbasi, Sanem and Ferhatosmanoglu, Hakan},
title = {Predicting Optimal Facility Location without Customer Locations},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098198},
doi = {10.1145/3097983.3098198},
abstract = {Deriving meaningful insights from location data helps businesses make better decisions. One critical decision made by a business is choosing a location for its new facility. Optimal location queries ask for a location to build a new facility that optimizes an objective function. Most of the existing works on optimal location queries propose solutions to return best location when the set of existing facilities and the set of customers are given. However, most businesses do not know the locations of their customers. In this paper, we introduce a new problem setting for optimal location queries by removing the assumption that the customer locations are known. We propose an optimal location predictor which accepts partial information about customer locations and returns a location for the new facility. The predictor generates synthetic customer locations by using given partial information and it runs optimal location queries with generated location data. Experiments with real data show that the predictor can find the optimal location when sufficient information is provided.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2121–2130},
numpages = {10},
keywords = {prediction, uncertainty, location analytics, optimal location queries, data generation},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098148,
author = {Yin, Zi and Chang, Keng-hao and Zhang, Ruofei},
title = {DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098148},
doi = {10.1145/3097983.3098148},
abstract = {Information extraction and user intention identification is a central topic in modern query understanding and recommendation systems. In this paper, we propose DeepProbe, a generic information-directed interaction framework which is built around an attention-based sequence to sequence (seq2seq) recurrent neural network. DeepProbe can rephrase, evaluate, and even actively ask questions, leveraging the generative ability and likelihood estimation made possible by seq2seq models. DeepProbe makes decisions based on a derived uncertainty (entropy) measure conditioned on user inputs, possibly with multiple rounds of interactions. Three applications, namely a rewritter, a relevance scorer and a chatbot for ad recommendation, were built around DeepProbe, with the first two serving as precursory building blocks for the third. We first use the seq2seq model in DeepProbe to rewrite a user query into one of standard query form, which is submitted to an ordinary recommendation system. Secondly, we evaluate DeepProbe's seq2seq model-based relevance scoring. Finally, we build a chatbot prototype capable of making active user interactions, which can ask questions that maximize information gain, allowing for a more efficient user intention idenfication process. We evaluate first two applications by 1) comparing with baselines by BLEU and AUC, and 2) human judge evaluation. Both demonstrate significant improvements compared with current state-of-the-art systems, proving their values as useful tools on their own, and at the same time laying a good foundation for the ongoing chatbot application.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2131–2139},
numpages = {9},
keywords = {rnn, probabilistic scoring, recommendation system, attention mechanism, deep learning, chatbot, sponsored search, online advertising, seq2seq, query rewriting, information gain},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098117,
author = {Zhang, Liheng and Aggarwal, Charu and Qi, Guo-Jun},
title = {Stock Price Prediction via Discovering Multi-Frequency Trading Patterns},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098117},
doi = {10.1145/3097983.3098117},
abstract = {Stock prices are formed based on short and/or long-term commercial and trading activities that reflect different frequencies of trading patterns. However, these patterns are often elusive as they are affected by many uncertain political-economic factors in the real world, such as corporate performances, government policies, and even breaking news circulated across markets. Moreover, time series of stock prices are non-stationary and non-linear, making the prediction of future price trends much challenging. To address them, we propose a novel State Frequency Memory (SFM) recurrent network to capture the multi-frequency trading patterns from past market data to make long and short term predictions over time. Inspired by Discrete Fourier Transform (DFT), the SFM decomposes the hidden states of memory cells into multiple frequency components, each of which models a particular frequency of latent trading pattern underlying the fluctuation of stock price. Then the future stock prices are predicted as a nonlinear mapping of the combination of these components in an Inverse Fourier Transform (IFT) fashion. Modeling multi-frequency trading patterns can enable more accurate predictions for various time ranges: while a short-term prediction usually depends on high frequency trading patterns, a long-term prediction should focus more on the low frequency trading patterns targeting at long-term return. Unfortunately, no existing model explicitly distinguishes between various frequencies of trading patterns to make dynamic predictions in literature. The experiments on the real market data also demonstrate more competitive performance by the SFM as compared with the state-of-the-art methods.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2141–2149},
numpages = {9},
keywords = {state frequency memory, stock price prediction, multi-frequency trading patterns},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098138,
author = {Zhang, Lingyu and Hu, Tao and Min, Yue and Wu, Guobin and Zhang, Junying and Feng, Pengcheng and Gong, Pinghua and Ye, Jieping},
title = {A Taxi Order Dispatch Model Based On Combinatorial Optimization},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098138},
doi = {10.1145/3097983.3098138},
abstract = {Taxi-booking apps have been very popular all over the world as they provide convenience such as fast response time to the users. The key component of a taxi-booking app is the dispatch system which aims to provide optimal matches between drivers and riders. Traditional dispatch systems sequentially dispatch taxis to riders and aim to maximize the driver acceptance rate for each individual order. However, the traditional systems may lead to a low global success rate, which degrades the rider experience when using the app. In this paper, we propose a novel system that attempts to optimally dispatch taxis to serve multiple bookings. The proposed system aims to maximize the global success rate, thus it optimizes the overall travel efficiency, leading to enhanced user experience. To further enhance users' experience, we also propose a method to predict destinations of a user once the taxi-booking APP is started. The proposed method employs the Bayesian framework to model the distribution of a user's destination based on his/her travel histories.We use rigorous A/B tests to compare our new taxi dispatch method with state-of-the-art models using data collected in Beijing. Experimental results show that the proposed method is significantly better than other state-of-the art models in terms of global success rate (increased from 80% to 84%). Moreover, we have also achieved significant improvement on other metrics such as user's waiting-time and pick-up distance. For our destination prediction algorithm, we show that our proposed model is superior to the baseline model by improving the top-3 accuracy from 89% to 93%. The proposed taxi dispatch and destination prediction algorithms are both deployed in our online systems and serve tens of millions of users everyday.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2151–2159},
numpages = {9},
keywords = {combinatorial optimization, destination prediction, taxi dispatch, circular distribution},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098143,
author = {Zheng, Guanjie and Brantley, Susan L. and Lauvaux, Thomas and Li, Zhenhui},
title = {Contextual Spatial Outlier Detection with Metric Learning},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098143},
doi = {10.1145/3097983.3098143},
abstract = {Hydraulic fracturing (or "fracking") is a revolutionary well stimulation technique for shale gas extraction, but has spawned controversy in environmental contamination. If methane from gas wells leaks extensively, this greenhouse gas can impact drinking water wells and enhance global warming. Our work is motivated by this heated debate on environmental issue and focuses on general data analytical techniques to detect anomalous spatial data samples (e.g., water samples related to potential leakages). Specifically, we propose a spatial outlier detection method based on contextual neighbors. Different from existing work, our approach utilizes both spatial attributes and non-spatial contextual attributes to define neighbors. We further use robust metric learning to combine different contextual attributes in order to find meaningful neighbors. Our technique can be applied to any spatial dataset. Extensive experimental results on five real-world datasets demonstrate the effectiveness of our approach. We also show some interesting case studies, including one case linking to leakage of a gas well.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2161–2170},
numpages = {10},
keywords = {outlier detection, metric learning},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098149,
author = {Zheng, Kaiping and Gao, Jinyang and Ngiam, Kee Yuan and Ooi, Beng Chin and Yip, Wei Luen James},
title = {Resolving the Bias in Electronic Medical Records},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098149},
doi = {10.1145/3097983.3098149},
abstract = {Electronic Medical Records (EMR) are the most fundamental resources used in healthcare data analytics. Since people visit hospital more frequently when they feel sick and doctors prescribe lab examinations when they feel necessary, we argue that there could be a strong bias in EMR observations compared with the hidden conditions of patients. Directly using such EMR for analytical tasks without considering the bias may lead to misinterpretation. To this end, we propose a general method to resolve the bias by transforming EMR to regular patient hidden condition series using a Hidden Markov Model (HMM) variant. Compared with the biased EMR series with irregular time stamps, the unbiased regular time series is much easier to be processed by most analytical models and yields better results. Extensive experimental results demonstrate that our bias resolving method imputes missing data more accurately than baselines and improves the performance of the state-of-the-art methods on typical medical data analytics.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2171–2180},
numpages = {10},
keywords = {data analytics, time series, healthcare},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098190,
author = {Zhou, Wubai and Xue, Wei and Baral, Ramesh and Wang, Qing and Zeng, Chunqiu and Li, Tao and Xu, Jian and Liu, Zheng and Shwartz, Larisa and Ya. Grabarnik, Genady},
title = {STAR: A System for Ticket Analysis and Resolution},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098190},
doi = {10.1145/3097983.3098190},
abstract = {In large scale and complex IT service environments, a problematic incident is logged as a ticket and contains the ticket summary (system status and problem description). The system administrators log the step-wise resolution description when such tickets are resolved. The repeating service events are most likely resolved by inferring similar historical tickets. With the availability of reasonably large ticket datasets, we can have an automated system to recommend the best matching resolution for a given ticket summary. In this paper, we first identify the challenges in real-world ticket analysis and develop an integrated framework to efficiently handle those challenges. The framework first quantifies the quality of ticket resolutions using a regression model built on carefully designed features. The tickets, along with their quality scores obtained from the resolution quality quantification, are then used to train a deep neural network ranking model that outputs the matching scores of ticket summary and resolution pairs. This ranking model allows us to leverage the resolution quality in historical tickets when recommending resolutions for an incoming incident ticket. In addition, the feature vectors derived from the deep neural ranking model can be effectively used in other ticket analysis tasks, such as ticket classification and clustering. The proposed framework is extensively evaluated with a large real-world dataset.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2181–2190},
numpages = {10},
keywords = {ticket resolution, it service management, sentence model, convolutional neural network},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

@inproceedings{10.1145/3097983.3098134,
author = {Zhu, Han and Jin, Junqi and Tan, Chang and Pan, Fei and Zeng, Yifan and Li, Han and Gai, Kun},
title = {Optimized Cost per Click in Taobao Display Advertising},
year = {2017},
isbn = {9781450348874},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3097983.3098134},
doi = {10.1145/3097983.3098134},
abstract = {Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to display in tens of milliseconds. Common pricing methods include cost per mille (CPM) and cost per click (CPC). Traditional advertising systems target certain traits of users and ad placements with fixed bids, essentially regarded as coarse-grained matching of bid and traffic quality. However, the fixed bids set by the advertisers competing for different quality requests cannot fully optimize the advertisers' key requirements. Moreover, the platform has to be responsible for the business revenue and user experience. Thus, we proposed a bid optimizing strategy called optimized cost per click (OCPC) which automatically adjusts the bid to achieve finer matching of bid and traffic quality of page view (PV) request granularity. Our approach optimizes advertisers' demands, platform business revenue and user experience and as a whole improves traffic allocation efficiency. We have validated our approach in Taobao display advertising system in production. The online A/B test shows our algorithm yields substantially better results than previous fixed bid manner.},
booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2191–2200},
numpages = {10},
keywords = {display advertising, probability estimation, bid optimization},
location = {Halifax, NS, Canada},
series = {KDD '17}
}

license_type: permissive

branch_name: refs/heads/main

extension: bib