Paper Digest: SIGIR 2017 Highlights

July 28, 2017June 26, 2020 admin

SIGIR (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval) is one of the top information retrieval conferences in the world.

To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.

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Paper Digest Team
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TABLE 1: SIGIR 2017 Papers

	Title	Authors	Highlight
1	Forward to the Past: Notes towards a Pre-history of Web Search	Stephen Robertson	Forward to the Past: Notes towards a Pre-history of Web Search
2	Mail Search: It’s Getting Personal!	Yoelle Maarek	Failure is evident when we can’t find a message that we remember having read, and this increases our frustration.
3	Navigating Imprecision in Relevance Assessments on the Road to Total Recall: Roger and Me	Gordon V. Cormack, Maura R. Grossman	Models are presented that better fit available data than the infallible ground-truth model.
4	Meta-evaluation of Online and Offline Web Search Evaluation Metrics	Ye Chen, Ke Zhou, Yiqun Liu, Min Zhang, Shaoping Ma	Offline metrics are usually based on relevance judgments of query-document pairs from assessors while online metrics exploit the user behavior data, such as clicks, collected from search engines to compare search algorithms.
5	The Probability that Your Hypothesis Is Correct, Credible Intervals, and Effect Sizes for IR Evaluation	Tetsuya Sakai	For both paired and unpaired tests, we propose that the IR community report the EAP, the credible interval, and the probability of hypothesis being true, not only for the raw difference in means but also for the effect size in terms of Glass’s Δ.
6	Can Deep Effectiveness Metrics Be Evaluated Using Shallow Judgment Pools?	Xiaolu Lu, Alistair Moffat, J. Shane Culpepper	Here we study the problem of metric score adjustment, with the goal of accurately estimating system performance when using deep metrics and limited judgment sets, assuming that dynamic score adjustment is required per topic due to the variability in the number of relevant documents.
7	Learning to Rank Using Localized Geometric Mean Metrics	Yuxin Su, Irwin King, Michael Lyu	In this paper, we propose a novel Riemannian metric learning algorithm to capture the local structures and develop a robust LtR algorithm.
8	End-to-End Neural Ad-hoc Ranking with Kernel Pooling	Chenyan Xiong, Zhuyun Dai, Jamie Callan, Zhiyuan Liu, Russell Power	This paper proposes K-NRM, a kernel based neural model for document ranking.
9	Neural Ranking Models with Weak Supervision	Mostafa Dehghani, Hamed Zamani, Aliaksei Severyn, Jaap Kamps, W. Bruce Croft	Hence, in this paper, we propose to train a neural ranking model using weak supervision, where labels are obtained automatically without human annotators or any external resources (e.g., click data).
10	Variational Deep Semantic Hashing for Text Documents	Suthee Chaidaroon, Yi Fang	In this paper, we propose a series of novel deep document generative models for text hashing.
11	ICE: Item Concept Embedding via Textual Information	Chuan-Ju Wang, Ting-Hsiang Wang, Hsiu-Wei Yang, Bo-Sin Chang, Ming-Feng Tsai	This paper proposes an item concept embedding (ICE) framework to model item concepts via textual information.
12	Leveraging Contextual Sentence Relations for Extractive Summarization Using a Neural Attention Model	Pengjie Ren, Zhumin Chen, Zhaochun Ren, Furu Wei, Jun Ma, Maarten de Rijke	We propose a neural network model, Contextual Relation-based Summarization (CRSum), to take advantage of contextual relations among sentences so as to improve the performance of sentence regression.
13	Stacking Bagged and Boosted Forests for Effective Automated Classification	Raphael Campos, Sérgio Canuto, Thiago Salles, Clebson C.A. de Sá, Marcos André Gonçalves	We show that BERT is among the top performers in the vast majority of analyzed cases, while retaining the unique benefits of RF classifiers (explainability, parallelization, easiness of parameterization).
14	Deep Learning for Extreme Multi-label Text Classification	Jingzhou Liu, Wei-Cheng Chang, Yuexin Wu, Yiming Yang	This paper presents the first attempt at applying deep learning to XMTC, with a family of new Convolutional Neural Network (CNN) models which are tailored for multi-label classification in particular.
15	Engaged or Frustrated?: Disambiguating Emotional State in Search	Ashlee Edwards, Diane Kelly	This research examines the differences in the search behaviors and physiologies of people who are engaged or frustrated during search.
16	A Study of Snippet Length and Informativeness: Behaviour, Performance and User Experience	David Maxwell, Leif Azzopardi, Yashar Moshfeghi	The design and presentation of a Search Engine Results Page (SERP) has been subject to much research.
17	Improving Exploratory Search Experience through Hierarchical Knowledge Graphs	Bahareh Sarrafzadeh, Edward Lank	In this paper, we explore a multi-layer extension to knowledge graphs, hierarchical knowledge graphs (HKGs), that combines hierarchical and network visualizations into a unified data representation.
18	Searching on the Go: The Effects of Fragmented Attention on Mobile Web Search Tasks	Morgan Harvey, Matthew Pointon	In this work we conducted a laboratory experiment with both device types in which we simulated everyday, common mobile situations that may cause fragmented attention, impact search performance and affect user perception.
19	User Interaction Sequences for Search Satisfaction Prediction	Rishabh Mehrotra, Imed Zitouni, Ahmed Hassan Awadallah, Ahmed El Kholy, Madian Khabsa	In this work, we focus on considering holistic view of user interaction with the search engine result page (SERP) and construct detailed universal interaction sequences of their activity.
20	Multi-site User Behavior Modeling and Its Application in Video Recommendation	Chunfeng Yang, Huan Yan, Donghan Yu, Yong Li, Dah Ming Chiu	In this work, we try to model user preferences in six popular video websites with user viewing records obtained from a large ISP in China.
21	Item Silk Road: Recommending Items from Information Domains to Social Users	Xiang Wang, Xiangnan He, Liqiang Nie, Tat-Seng Chua	In this work, we address the problem of cross-domain social recommendation, i.e., recommending relevant items of information domains to potential users of social networks.
22	Cross-Domain Recommendation via Clustering on Multi-Layer Graphs	Aleksandr Farseev, Ivan Samborskii, Andrey Filchenkov, Tat-Seng Chua	Taking into account these two aspects, we introduce a novel cross-network collaborative recommendation framework C³R, which utilizes both individual and group knowledge, while being trained on data from multiple social media sources.
23	Optimizing Trade-offs Among Stakeholders in Real-Time Bidding by Incorporating Multimedia Metrics	Xiang Chen, Bowei Chen, Mohan Kankanhalli	In this paper, we propose a two-stage computational framework that selects a banner ad based on the optimized trade-offs among all stakeholders.
24	A Probabilistic Reformulation of Memory-Based Collaborative Filtering: Implications on Popularity Biases	Rocío Cañamares, Pablo Castells	We develop a probabilistic formulation giving rise to a formal version of heuristic k nearest-neighbor (kNN) collaborative filtering.
25	Deep Semantic Hashing with Generative Adversarial Networks	Zhaofan Qiu, Yingwei Pan, Ting Yao, Tao Mei	This paper studies the exploration of generating synthetic data through semi-supervised generative adversarial networks (GANs), which leverages largely unlabeled and limited labeled training data to produce highly compelling data with intrinsic invariance and global coherence, for better understanding statistical structures of natural data.
26	Characterizing and Predicting Enterprise Email Reply Behavior	Liu Yang, Susan T. Dumais, Paul N. Bennett, Ahmed Hassan Awadallah	In this paper, we extend previous work on predicting email reply behavior by looking at enterprise settings and considering more than dyadic communications.
27	ReAct: Online Multimodal Embedding for Recency-Aware Spatiotemporal Activity Modeling	Chao Zhang, Keyang Zhang, Quan Yuan, Fangbo Tao, Luming Zhang, Tim Hanratty, Jiawei Han	We propose ReAct, a method that processes continuous GTSM streams and obtains recency-aware spatiotemporal activity models on the fly.
28	EntiTables: Smart Assistance for Entity-Focused Tables	Shuo Zhang, Krisztian Balog	We introduce and focus on two specifc tasks: populating rows with additional instances (entities) and populating columns with new headings.
29	Understanding and Modeling Success in Email Search	Jin Young Kim, Nick Craswell, Susan Dumais, Filip Radlinski, Fang Liu	In this study, we built an opt-in client application which monitors a user’s email search activity and then pops up an in-situ survey when a search session is finished.
30	Investigating Examination Behavior of Image Search Users	Xiaohui Xie, Yiqun Liu, Xiaochuan Wang, Meng Wang, Zhijing Wu, Yingying Wu, Min Zhang, Shaoping Ma	To shed light on this research question, we conducted an eye-tracking study to investigate users’ examination behavior in image searches.
31	Extracting Hierarchies of Search Tasks & Subtasks via a Bayesian Nonparametric Approach	Rishabh Mehrotra, Emine Yilmaz	To this end, we propose an efficient Bayesian nonparametric model for extracting hierarchies of such tasks & subtasks.
32	Using Information Scent to Understand Mobile and Desktop Web Search Behavior	Kevin Ong, Kalervo Järvelin, Mark Sanderson, Falk Scholer	This paper investigates if Information Foraging Theory can be used to understand differences in user behavior when searching on mobile and desktop web search systems.
33	Deep Character-Level Click-Through Rate Prediction for Sponsored Search	Bora Edizel, Amin Mantrach, Xiao Bai	In this paper, we propose two novel approaches (one working at character level and the other working at word level) that use deep convolutional neural networks to predict the click-through rate of a query-advertisement pair.
34	Personalized Key Frame Recommendation	Xu Chen, Yongfeng Zhang, Qingyao Ai, Hongteng Xu, Junchi Yan, Zheng Qin	In this paper, we propose and investigate the problem of personalized key frame recommendation to bridge the above gap.
35	Personalized Itinerary Recommendation with Queuing Time Awareness	Kwan Hui Lim, Jeffrey Chan, Shanika Karunasekera, Christopher Leckie	To solve these challenges, we propose the PersQ algorithm for recommending personalized itineraries that take into consideration attraction popularity, user interests and queuing times.
36	Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention	Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, Tat-Seng Chua	In this paper, we introduce a novel attention mechanism in CF to address the challenging item- and component-level implicit feedback in multimedia recommendation, dubbed Attentive Collaborative Filtering (ACF).
37	Neural Rating Regression with Abstractive Tips Generation for Recommendation	Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, Wai Lam	We propose a deep learning based framework named NRT which can simultaneously predict precise ratings and generate abstractive tips with good linguistic quality simulating user experience and feelings.
38	Neural Factorization Machines for Sparse Predictive Analytics	Xiangnan He, Tat-Seng Chua	In this paper, we propose a novel model Neural Factorization Machine (NFM) for prediction under sparse settings.
39	Accounting for the Correspondence in Commented Data	Renqin Cai, Chi Wang, Hongning Wang	In this work, we develop a Commented Correspondence Topic Model to model correspondence in commented text data.
40	Jointly Learning Word Embeddings and Latent Topics	Bei Shi, Wai Lam, Shoaib Jameel, Steven Schockaert, Kwun Ping Lai	In this paper, we propose STE, a framework which can learn word embeddings and latent topics in a unified manner.
41	On the Power Laws of Language: Word Frequency Distributions	Flavio Chierichetti, Ravi Kumar, Bo Pang	A simple generative model is proposed to capture this phenomenon.
42	Retrieval Consistency in the Presence of Query Variations	Peter Bailey, Alistair Moffat, Falk Scholer, Paul Thomas	In this paper we examine the retrieval consistency of a set of five systems responding to syntactic query variations over one hundred topics, working with the UQV100 test collection, and using Rank-Biased Overlap (RBO) relative to a centroid ranking over the query variations per topic as a measure of consistency.
43	Comparing In Situ and Multidimensional Relevance Judgments	Jiepu Jiang, Daqing He, James Allan	The second one collects multidimensional assessments to complement relevance or usefulness judgments, with four distinct alternative aspects examined in this paper – novelty, understandability, reliability, and effort.
44	Online In-Situ Interleaved Evaluation of Real-Time Push Notification Systems	Adam Roegiest, Luchen Tan, Jimmy Lin	We describe a user study of such systems in the context of the TREC 2016 Real-Time Summarization Track, where system updates are immediately delivered as push notifications to the mobile devices of a cohort of users.
45	Evaluating Web Search with a Bejeweled Player Model	Fan Zhang, Yiqun Liu, Xin Li, Min Zhang, Yinghui Xu, Shaoping Ma	Inspired by a popular computer game named Bejeweled, we propose a Bejeweled Player Model (BPM) to simulate users’ search interaction processes and evaluate their search performances.
46	Evaluating Mobile Search with Height-Biased Gain	Cheng Luo, Yiqun Liu, Tetsuya Sakai, Fan Zhang, Min Zhang, Shaoping Ma	Based on these findings, we propose a new evaluation metric, Height-Biased Gain, which is calculated by summing up the product of gain distribution and discount factors that are both modeled in terms of result height.
47	Efficient Cost-Aware Cascade Ranking in Multi-Stage Retrieval	Ruey-Cheng Chen, Luke Gallagher, Roi Blanco, J. Shane Culpepper	In this paper, we re-examine the importance of tightly integrating feature costs into multi-stage learning-to-rank (LTR) IR systems.
48	Computational Social Indicators: A Case Study of Chinese University Ranking	Fuli Feng, Liqiang Nie, Xiang Wang, Richang Hong, Tat-Seng Chua	Towards this end, we present a novel graph-based multi-channel ranking scheme for social indicator computation by exploring the rich multi-channel Web data.
49	Information Retrieval Meets Game Theory: The Ranking Competition Between Documents’ Authors	Nimrod Raifer, Fiana Raiber, Moshe Tennenholtz, Oren Kurland	We present a novel theoretical and empirical analysis of the strategic behavior of publishers using these foundations.
50	On Application of Learning to Rank for E-Commerce Search	Shubhra Kanti Karmaker Santu, Parikshit Sondhi, ChengXiang Zhai	In this paper, we discuss the practical challenges in applying learning to rank methods to E-Com search, including the challenges in feature representation, obtaining reliable relevance judgments, and optimally exploiting multiple user feedback signals such as click rates, add-to-cart ratios, order rates, and revenue.
51	Intent-Aware Semantic Query Annotation	Rafael Glater, Rodrygo L.T. Santos, Nivio Ziviani	In this paper, we propose a framework for learning semantic query annotations suitable to the target intent of each individual query.
52	Efficient & Effective Selective Query Rewriting with Efficiency Predictions	Craig Macdonald, Nicola Tonellotto, Iadh Ounis	In this paper, we propose a novel framework for using the predicted execution time of various query rewritings to select between alternatives on a per-query basis, in a manner that ensures both effectiveness and efficiency.
53	Relevance-based Word Embedding	Hamed Zamani, W. Bruce Croft	In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query.
54	IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models	Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, Dell Zhang	We propose a game theoretical minimax game to iteratively optimise both models.
55	Personalized PageRank in Uncertain Graphs with Mutually Exclusive Edges	Jung Hyun Kim, Mao-Lin Li, K. Selçuk Candan, Maria Luisa Sapino	To tackle with this challenge, in this paper, we propose an efficient Uncertain Personalized PageRank (UPPR) algorithm to approximately compute personalized PageRank values on an uncertain graph with edge uncertainties.
56	Adapting Markov Decision Process for Search Result Diversification	Long Xia, Jun Xu, Yanyan Lan, Jiafeng Guo, Wei Zeng, Xueqi Cheng	In this paper we address the issue of learning diverse ranking models for search result diversification.
57	Learning to Diversify Search Results via Subtopic Attention	Zhengbao Jiang, Ji-Rong Wen, Zhicheng Dou, Wayne Xin Zhao, Jian-Yun Nie, Ming Yue	In this paper, we propose a learning framework for explicit result diversification where subtopics are explicitly modeled.
58	Retrieval Algorithms Optimized for Human Learning	Rohail Syed, Kevyn Collins-Thompson	We address this problem by introducing a novel theoretical framework, algorithms, and empirical analysis of an information retrieval model that is optimized for learning outcomes instead of generic relevance.
59	Content Recommendation for Viral Social Influence	Sergei Ivanov, Konstantinos Theocharidis, Manolis Terrovitis, Panagiotis Karras	In this paper, we address the natural problem that arises in such circumstances: Suggest content, expressed as a limited set of attributes, for a creative promotion campaign that starts out from a given seed set of initiators, so as to maximize its expected spread over a social network.
60	Exploiting Food Choice Biases for Healthier Recipe Recommendation	David Elsweiler, Christoph Trattner, Morgan Harvey	In this paper, using insights gained from various data sources, we explore the feasibility of substituting meals that would typically be recommended to users with similar, healthier dishes.
61	Embedding Factorization Models for Jointly Recommending Items and User Generated Lists	Da Cao, Liqiang Nie, Xiangnan He, Xiaochi Wei, Shunzhi Zhu, Tat-Seng Chua	Specifically, we employ factorization model to capture users’ preferences over items and lists, and utilize embedding-based models to discover the co-occurrence information among items and lists.
62	Classification by Retrieval: Binarizing Data and Classifiers	Fumin Shen, Yadong Mu, Yang Yang, Wei Liu, Li Liu, Jingkuan Song, Heng Tao Shen	This paper proposes a generic formulation that significantly expedites the training and deployment of image classification models, particularly under the scenarios of many image categories and high feature dimensions.
63	BitFunnel: Revisiting Signatures for Search	Bob Goodwin, Michael Hopcroft, Dan Luu, Alex Clemmer, Mihaela Curmei, Sameh Elnikety, Yuxiong He	This paper describes algorithmic innovations and changes in the cloud computing landscape that led us to reconsider and eventually field a technology that was once considered unusable.
64	Efficient Data Structures for Massive	Giulio Ermanno Pibiri, Rossano Venturini	In this paper we study the problem of reducing the space required by the representation of such datasets, maintaining the capability of looking up for a given N-gram within micro seconds.
65	Faster BlockMax WAND with Variable-sized Blocks	Antonio Mallia, Giuseppe Ottaviano, Elia Porciani, Nicola Tonellotto, Rossano Venturini	We introduce a refinement for BlockMaxWAND that uses variable- sized blocks, rather than constant-sized.
66	LoSHa: A General Framework for Scalable Locality Sensitive Hashing	Jinfeng Li, James Cheng, Fan Yang, Yuzhen Huang, Yunjian Zhao, Xiao Yan, Ruihao Zhao	We propose LoSHa, a distributed computing framework that reduces the development cost by designing a tailor-made, general programming interface and achieves high efficiency by exploring LSH-specific system implementation and optimizations.
67	Learning a Hierarchical Embedding Model for Personalized Product Search	Qingyao Ai, Yongfeng Zhang, Keping Bi, Xu Chen, W. Bruce Croft	In this paper, we propose a hierarchical embedding model to learn semantic representations for entities (i.e. words, products, users and queries) from different levels with their associated language data. Following the methodology of previous studies, we constructed personalized product search benchmarks with Amazon product data.
68	Exploring User-Specific Information in Music Retrieval	Zhiyong Cheng, Jialie Shen, Liqiang Nie, Tat-Seng Chua, Mohan Kankanhalli	In this paper, we propose a novel model, named User-Information-Aware Music Interest Topic (UIA-MIT) model.
69	The Utility and Privacy Effects of a Click	Rachid Guerraoui, Anne-Marie Kermarrec, Mahsa Taziki	In this paper, for the first time, we propose a way to quantify the exact utility and privacy effects of each user click.
70	Privacy through Solidarity: A User-Utility-Preserving Framework to Counter Profiling	Asia J. Biega, Rishiraj Saha Roy, Gerhard Weikum	In this work, we propose a framework which leverages solidarity in a large community to scramble user interaction histories.
71	Joint Learning of Response Ranking and Next Utterance Suggestion in Human-Computer Conversation System	Rui Yan, Dongyan Zhao, Weinan E.	In this paper, we propose a new task for conversation systems: joint learning of response ranking featured with next utterance suggestion.
72	Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture	Yi Tay, Minh C. Phan, Luu Anh Tuan, Siu Cheung Hui	We describe a new deep learning architecture for learning to rank question answer pairs.
73	Incomplete Follow-up Question Resolution using Retrieval based Sequence to Sequence Learning	Vineet Kumar, Sachindra Joshi	In this work, we present a retrieval based sequence to sequence learning system that can generate the complete (or intended) question for an incomplete follow-up question (given the conversation context).
74	Modelling Information Needs in Collaborative Search Conversations	Sosuke Shiga, Hideo Joho, Roi Blanco, Johanne R. Trippas, Mark Sanderson	The contribution of this work is three-fold.
75	Predicting Which Topics You Will Join in the Future on Social Media	Haoran Huang, Qi Zhang, Jindou Wu, Xuanjing Huang	In this study, we investigate the problem of predicting whether a user will join a topic based on his posting history. To train and evaluate the proposed method, we collected a large-scale dataset from Twitter.
76	What Are You Known For?: Learning User Topical Profiles with Implicit and Explicit Footprints	Cheng Cao, Hancheng Ge, Haokai Lu, Xia Hu, James Caverlee	In this paper, we propose a unified model for learning user topical profiles that simultaneously considers multiple footprints.
77	Hierarchical Community-Level Information Diffusion Modeling in Social Networks	Yuan Zhang, Tianshu Lyu, Yan Zhang	In this paper, we propose a Hierarchical Community-level Information Diffusion (HCID) model to capture the information diffusion process in social networks.
78	Word-Entity Duet Representations for Document Ranking	Chenyan Xiong, Jamie Callan, Tie-Yan Liu	This paper presents a word-entity duet framework for utilizing knowledge bases in ad-hoc retrieval.
79	Dynamic Factual Summaries for Entity Cards	Faegheh Hasibi, Krisztian Balog, Svein Erik Bratsberg	In this paper, we make the first effort towards generating and evaluating such factual summaries. We introduce and address the novel problem of dynamic entity summarization for entity cards, and break it down to two specific subtasks: fact ranking and summary generation.
80	MEmbER: Max-Margin Based Embeddings for Entity Retrieval	Shoaib Jameel, Zied Bouraoui, Steven Schockaert	We propose a new class of methods for learning vector space embeddings of entities.
81	On the Reusability of "Living Labs" Test Collections: A Case Study of Real-Time Summarization	Luchen Tan, Gaurav Baruah, Jimmy Lin	In this paper, we performed a "leave-one-out" analysis of human judgment data derived from the TREC 2016 Real-Time Summarization Track and show that those judgments do not appear to be reusable.
82	Automatically Extracting High-Quality Negative Examples for Answer Selection in Question Answering	Haotian Zhang, Jinfeng Rao, Jimmy Lin, Mark D. Smucker	We propose a heuristic called "one answer per document" for automatically extracting high-quality negative examples for answer selection in question answering.
83	Leveraging Cross-Network Information for Graph Sparsification in Influence Maximization	Xiao Shen, Fu-lai Chung, Sitong Mao	In this work, a Cross-Network Graph Sparsification (CNGS) model is proposed to leverage the influence backbone knowledge pre-detected in a source network to predict and remove the edges least likely to contribute to the influence propagation in the target networks.
84	Combining Top-N Recommenders with Metasearch Algorithms	Daniel Valcarce, Javier Parapar, Álvaro Barreiro	In this paper, we explore methods for combining multiple recommendation approaches.
85	Counter Deanonymization Query: H-index Based	Jianliang Gao, Bo Song, Zheng Chen, Weimao Ke, Wanying Ding, Xiaohua Hu	In this paper, we propose a novel k-anonymization scheme to counter deanonymization queries on social networks.
86	SPOT: Selecting occuPations frOm Trajectories	Peipei Li, Junjie Yao, Liping Wang, Xuemin Lin	This paper proposes a novel approach, i.e., SPOT (Selecting occuPation frOm Trajectories).
87	Personalized Query Suggestion Diversification	Wanyu Chen, Fei Cai, Honghui Chen, Maarten de Rijke	We propose a personalized query suggestion diversification model (PQSD), where a user’s long-term search behavior is injected into a basic greedy query suggestion diversification model (G-QSD) that considers a user’s search context in their current session.
88	Weighted Domain Translation for Online News Comments Emotion Tagging	Ying Zhang, Li Yu, Xue Zhao, Xiaojie Yuan, Lei Xu	In this paper, we accomplish cross-domain emotion tagging based on an advanced neural network BLSTM (bidirectional long short-term memory) with "domain translation”, which can overcome the difference between domains.
89	From Footprint to Friendship: Modeling User Followership in Mobile Social Networks from Check-in Data	Cheng Wang, Jieren Zhou, Bo Yang	In this paper we aim at addressing the correlation between two critical factors in mobile social networks (MSNs): the social-relationship networking among users and the spatial mobility pattern of users.
90	Video Question Answering via Attribute-Augmented Attention Network Learning	Yunan Ye, Zhou Zhao, Yimeng Li, Long Chen, Jun Xiao, Yueting Zhuang	In this paper, we study the problem of video question answering by modeling its temporal dynamics with frame-level attention mechanism.
91	Learning Max-Margin GeoSocial Multimedia Network Representations for Point-of-Interest Suggestion	Zhou Zhao, Qifan Yang, Hanqing Lu, Min Yang, Jun Xiao, Fei Wu, Yueting Zhuang	In this paper, we consider the problem of POI suggestion from the viewpoint of learning geosocial multimedia network representations.
92	Learning To Rank Resources	Zhuyun Dai, Yubin Kim, Jamie Callan	We present a learning-to-rank approach for resource selection.
93	DeepStyle: Learning User Preferences for Visual Recommendation	Qiang Liu, Shu Wu, Liang Wang	Accordingly, we propose a DeepStyle method for learning style features of items and sensing preferences of users.
94	Target Type Identification for Entity-Bearing Queries	Darío Garigliotti, Faegheh Hasibi, Krisztian Balog	In this work, we address the problem of automatically detecting the target types of a query with respect to a type taxonomy.
95	Query Expansion for Email Search	Saar Kuzi, David Carmel, Alex Libov, Ariel Raviv	Three state-of-the-art expansion methods are examined: 1) a global translation-based expansion model; 2) a personalized-based word embedding model; 3) the classical pseudo-relevance-feedback model.
96	Generating Clinical Queries from Patient Narratives: A Comparison between Machines and Humans	Bevan Koopman, Liam Cripwell, Guido Zuccon	This paper investigates how automated query generation methods can be used to derive effective ad-hoc queries from verbose patient narratives.
97	Centered kNN Graph for Semi-Supervised Learning	Ikumi Suzuki, Kazuo Hara	So we present a new graph construction method, the centered kNN graph, which not only reduces hub nodes but also avoids the over sparsification problem.
98	Event Recommendation based on Graph Random Walking and History Preference Reranking	Shenghao Liu, Bang Wang, Minghua Xu	In this paper, we study how to exploit diverse relations in an EBSN as well as individual history preferences to recommend preferred events.
99	An Extended Relevance Model for Session Search	Nir Levine, Haggai Roitman, Doron Cohen	We propose an extended relevance model that captures the user’s dynamic information need in the session.
100	An Enhanced Approach to Query Performance Prediction Using Reference Lists	Haggai Roitman	In this work, we try to fill the gaps.
101	Autonomous Crowdsourcing through Human-Machine Collaborative Learning	Azad Abad, Moin Nabi, Alessandro Moschitti	In this paper, we introduce a general iterative human-machine collaborative method for training crowdsource workers: the classifier (i.e., the machine) selects the highest quality examples for training the crowdsource workers (i.e., the humans).
102	Game State Retrieval with Keyword Queries	Atsushi Ushiku, Shinsuke Mori, Hirotaka Kameko, Yoshimasa Tsuruoka	In this work, we propose a search system that allows users to retrieve game states from a game record database by using keywords.
103	Improving Search Engines via Large-Scale Physiological Sensing	Ryen W. White, Ryan Ma	In this paper, we focus on heart rate and show that there are strong relationships between heart rate and various measures of user interest in a search result.
104	A Poisson Regression Method for Top-N Recommendation	Jiajin Huang, Jian Wang, Ning Zhong	As a user’s decision may be affected by correlations among items, we incorporate such correlations with the user and item latent factors to propose a Poisson-regression-based method for top-N recommendation tasks.
105	A Hierarchical Multimodal Attention-based Neural Network for Image Captioning	Yong Cheng, Fei Huang, Lian Zhou, Cheng Jin, Yuejie Zhang, Tao Zhang	The main contribution of our work is that the hierarchical structure and multimodal attention mechanism is both applied, thus each caption word can be generated with the multimodal attention on the intermediate semantic objects and the global visual content.
106	Unifying Multi-Source Social Media Data for Personalized Travel Route Planning	Gang Hu, Jie Shao, Fumin Shen, Zi Huang, Heng Tao Shen	This paper presents an approach which mines the user interest model by multi-source social media (e.g., travelogues and check-in records), and understands the user’s real intention by active behavior such as point of interest (POI) inputs.
107	LiveMaps: Converting Map Images into Interactive Maps	Michael R. Evans, Dragomir Yankov, Pavel Berkhin, Pavel Yudin, Florin Teodorescu, Wei Wu	In this paper, we describe a novel system, LiveMaps, for analyzing and retrieving an appropriate map viewport for a given image of a map.
108	Sub-corpora Impact on System Effectiveness	Nicola Ferro, Mark Sanderson	We found that sub-corpora are a significant effect.
109	Automatic and Semi-Automatic Document Selection for Technology-Assisted Review	Maura R. Grossman, Gordon V. Cormack, Adam Roegiest	In this work, we investigate the extent to which the observed effectiveness of the different methods may be confounded by chance, by inconsistent adherence to the Track guidelines, by selection bias in the evaluation method, or by discordant relevance assessments.
110	Recommending Complementary Products in E-Commerce Push Notifications with a Mixture Model Approach	Huasha Zhao, Luo Si, Xiaogang Li, Qiong Zhang	This paper proposes a mixture model approach for predicting push message open rate for a post-purchase complementary product recommendation task.
111	Neural Network based Reinforcement Learning for Real-time Pushing on Text Stream	Haihui Tan, Ziyu Lu, Wenjie Li	In this paper, we formulate the real-time pushing on text stream as a sequential decision making problem and propose a Neural Network based Reinforcement Learning (NNRL) algorithm for real-time decision making, e.g., push or skip the incoming text, with considering both history dependencies and future uncertainty.
112	Joint Latent Subspace Learning and Regression for Cross-Modal Retrieval	Jianlong Wu, Zhouchen Lin, Hongbin Zha	We conduct extensive experiments on multiple benchmark datasets and our proposed method outperforms the state-of-the-art approaches.
113	Label Aggregation for Crowdsourcing with Bi-Layer Clustering	Jing Zhang, Victor S. Sheng, Tao Li	This paper proposes a novel general label aggregation method for both binary and multi-class labeling in crowdsourcing, namely Bi-Layer Clustering (BLC), which clusters two layers of features – the conceptual-level and the physical-level features – to infer true labels of instances.
114	Effective Music Feature NCP: Enhancing Cover Song Recognition with Music Transcription	Yao Cheng, Xiaoou Chen, Deshun Yang, Xiaoshuo Xu	In this paper, we proposed a similar but more effective feature Note Class Profile (NCP) derived with music transcription techniques.
115	Deep Multimodal Embedding Model for Fine-grained Sketch-based Image Retrieval	Fei Huang, Yong Cheng, Cheng Jin, Yuejie Zhang, Tao Zhang	In this paper, we consider Fine-grained SBIR as a cross-modal retrieval problem and propose a deep multimodal embedding model that exploits all the beneficial multimodal information sources in sketches and images.
116	Cross-Device User Linking: URL, Session, Visiting Time, and Device-log Embedding	Minh C. Phan, Aixin Sun, Yi Tay	In this paper, we present insightful analysis on the dataset and propose a solution to link users based on their visited URLs, visiting time, and profile embeddings.
117	Mailbox-Based vs. Log-Based Query Completion for Mail Search	Michal Horovitz, Liane Lewin-Eytan, Alex Libov, Yoelle Maarek, Ariel Raviv	We therefore propose here to leverage the mailbox content in order to generate suggestions, taking advantage of mail-specific features.
118	The Impact of Linkage Methods in Hierarchical Clustering for Active Learning to Rank	Ziming Li, Maarten de Rijke	We propose a sampling method which selects a set of instances and labels the full set only once before training the ranking model.
119	Reinforcement Learning to Rank with Markov Decision Process	Zeng Wei, Jun Xu, Yanyan Lan, Jiafeng Guo, Xueqi Cheng	In this paper, we propose a novel learning to rank model on the basis of Markov decision process (MDP), referred to as MDPRank.
120	A Comparative Live Evaluation of Multileaving Methods on a Commercial cQA Search	Tomohiro Manabe, Akiomi Nishida, Makoto P. Kato, Takehiro Yamamoto, Sumio Fujita	We present one of the world’s first attempts to examine the feasibility of multileaving evaluation of document rankings on a large scale commercial community Question Answering (cQA) service.
121	Support for Interactive Identification of Mentioned Entities in Conversational Speech	Ning Gao, Douglas W. Oard, Mark Dredze	This paper describes our initial experiments with a freely available collection of Enron telephone conversations.
122	AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders	Shuai Zhang, Lina Yao, Xiwei Xu	In this paper, we propose a new hybrid model by generalizing contractive auto-encoder paradigm into matrix factorization framework with good scalability and computational efficiency, which jointly models content information as representations of effectiveness and compactness, and leverage implicit user feedback to make accurate recommendations.
123	Unsupervised Query-Focused Multi-Document Summarization using the Cross Entropy Method	Guy Feigenblat, Haggai Roitman, Odellia Boni, David Konopnicki	We present a novel unsupervised query-focused multi-document summarization approach.
124	Translation of Natural Language Query Into Keyword Query Using a RNN Encoder-Decoder	Hyun-Je Song, A-Yeong Kim, Seong-Bae Park	This paper proposes a novel method to translate a natural language query into a keyword query relevant to the natural language query for retrieving better search results without change of the engines.
125	Predictive Network Representation Learning for Link Prediction	Zhitao Wang, Chengyao Chen, Wenjie Li	In this paper, we propose a predictive network representation learning (PNRL) model to solve the structural link prediction problem.
126	Sentence-level Sentiment Classification with Weak Supervision	Fangzhao Wu, Jia Zhang, Zhigang Yuan, Sixing Wu, Yongfeng Huang, Jun Yan	In this paper, we propose an approach for sentence-level sentiment classification without the need of sentence labels.
127	Predicting Session Length in Media Streaming	Theodore Vasiloudis, Hossein Vahabi, Ross Kravitz, Valery Rashkov	In this work we present the first analysis of session length in a mobile-focused online service, using a real world data-set from a major music streaming service.
128	Exploring the Query Halo Effect in Site Search: Leading People to Longer Queries	Djoerd Hiemstra, Claudia Hauff, Leif Azzopardi	In this paper, we test whether a similar increase is observed when the same component is deployed in a production system for site search and used by real end users.
129	Top-N Recommendation with High-Dimensional Side Information via Locality Preserving Projection	Yifan Chen, Xiang Zhao, Maarten de Rijke	In this paper, we leverage high-dimensional side information to enhance top-N recommendations.
130	Applying Information Extraction for Patent Structure Analysis	Masayuki Okamoto, Zifei Shan, Ryohei Orihara	In this paper, we propose an information-extraction-based technique to grasp the patent claim structure.
131	Enhancing Recurrent Neural Networks with Positional Attention for Question Answering	Qin Chen, Qinmin Hu, Jimmy Xiangji Huang, Liang He, Weijie An	Based on this assumption, we propose a positional attention based RNN model, which incorporates the positional context of the question words into the answers’ attentive representations.
132	Event Early Embedding: Predicting Event Volume Dynamics at Early Stage	Zhiwei Liu, Yang Yang, Zi Huang, Fumin Shen, Dongxiang Zhang, Heng Tao Shen	In order to overcome these two problems, in this paper, we design an event early embedding model (EEEM) that can 1) extract social events from noise, 2) find the previous similar events, and 3) predict future dynamics of a new event.
133	POI Popularity Prediction via Hierarchical Fusion of Multiple Social Clues	Yaqian Duan, Xinze Wang, Yang Yang, Zi Huang, Ning Xie, Heng Tao Shen	In this paper, we propose a novel approach, termed Hierarchical Multi-Clue Fusion (HMCF), for predicting the popularity of POIs. We collect a multi-source POI dataset from four widely-used tourism platforms.
134	Multitask Learning for Fine-Grained Twitter Sentiment Analysis	Georgios Balikas, Simon Moura, Massih-Reza Amini	We argue that such classification tasks are correlated and we propose a multitask approach based on a recurrent neural network that benefits by jointly learning them.
135	Evolution of Information Needs based on Life Event Experiences with Topic Transition	Naoto Takeda, Yohei Seki, Mimpei Morishita, Yoichi Inagaki	We propose a method to clarify the evolution of users’ information needs related to a user’s interests and actions based upon life events such as "childbirth."
136	Distribution-oriented Aesthetics Assessment for Image Search	Chaoran Cui, Huidi Fang, Xiang Deng, Xiushan Nie, Hongshuai Dai, Yilong Yin	In this paper, distinguished from existing studies relying on a single label, we propose to quantify the image aesthetics by a distribution over quality levels.
137	On the Benefit of Incorporating External Features in a Neural Architecture for Answer Sentence Selection	Ruey-Cheng Chen, Evi Yulianti, Mark Sanderson, W. Bruce Croft	Incorporating conventional, unsupervised features into a neural architecture has the potential to improve modeling effectiveness, but this aspect is often overlooked in the research of deep learning models for information retrieval.
138	Personalized Response Generation via Domain adaptation	Min Yang, Zhou Zhao, Wei Zhao, Xiaojun Chen, Jia Zhu, Lianqiang Zhou, Zigang Cao	In this paper, we propose a novel personalized response generation model via domain adaptation (PRG-DM).
139	An Accurate, Efficient, and Scalable Approach to Channel Matching in Smart TVs	Jiwon Hong, Sang-Wook Kim, Mina Rho, YoonHee Choi, Yoonsik Tak	In this paper, we introduce our TV channel matching system that resolves such problems.
140	Top-K Influential Nodes in Social Networks: A Game Perspective	Yu Zhang, Yan Zhang	In this paper, we study influence maximization from a game perspective.
141	Online Learning to Rank for Cross-Language Information Retrieval	Razieh Rahimi, Azadeh Shakery	In this work, we present the first empirical study of optimizing a model for Cross-Language Information Retrieval (CLIR) based on implicit feedback inferred from user interactions.
142	Mining Business Opportunities from Location-based Social Networks	Shenglin Zhao, Irwin King, Michael R. Lyu, Jia Zeng, Mingxuan Yuan	In this paper, we take this challenge and define the business opportunity mining problem, which recommends new business categories at a partitioned business district.
143	On Including the User Dynamic in Learning to Rank	Nicola Ferro, Claudio Lucchese, Maria Maistro, Raffaele Perego	In this paper, we explore the possibility of integrating the user dynamic directly into the LtR algorithms.
144	Document Expansion Using External Collections	Garrick Sherman, Miles Efron	Our experiments demonstrate that the proposed model improves ad-hoc document retrieval effectiveness on a variety of corpus types, with a particular benefit on more heterogeneous collections of documents.
145	Detecting Positive Medical History Mentions	Bing Bai, Pierre-Francois Laquerre, Richard Jackson, Robert Stewart	In this work we designed a scheme to automatically extract the medical history of patients from a large healthcare database.
146	Emotional Social Signals for Search Ranking	Ismail Badache, Mohand Boughanem	Our objective in this paper is to study the impact of the new social signals, called Facebook reactions (love, haha, angry, wow, sad) in the retrieval.
147	Skill Translation Models in Expert Finding	Arash Dargahi Nobari, Sajad Sotudeh Gharebagh, Mahmood Neshati	In this paper, we propose two translation models to augment a given query with relevant words.
148	A Metric for Sentence Ordering Assessment Based on Topic-Comment Structure	Liana Ermakova, Josiane Mothe, Anton Firsov	In contrast to that, we present a self-sufficient metric for SO assessment based on text topic-comment structure.
149	Gaussian Embeddings for Collaborative Filtering	Ludovic Dos Santos, Benjamin Piwowarski, Patrick Gallinari	In this paper, we leverage recent works in learning Gaussian embeddings for the recommendation task.
150	Detecting Controversies in Online News Media	Kaspar Beelen, Evangelos Kanoulas, Bob van de Velde	This paper sets out to detect controversial news reports using online discussions as a source of information.
151	Generating and Personalizing Bundle Recommendations on	Apurva Pathak, Kshitiz Gupta, Julian McAuley	In this paper, we seek to understand the semantics of what constitutes a ‘good’ bundle, in order to recommend existing bundles to users on the basis of their constituent products, as well the more difficult task of generating new bundles that are personalized to a user. To do so we collect a new dataset from the Steam video game distribution platform, which is unique in that it contains both ‘traditional’ recommendation data (rating and purchase histories between users and items), as well as bundle purchase information.
152	X-DART: Blending Dropout and Pruning for Efficient Learning to Rank	Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Salvatore Trani	In this paper we propose X-DART, a new Learning to Rank algorithm focusing on the training of robust and compact ranking models.
153	Non-negative Matrix Factorization Meets Word Embedding	Melissa Ailem, Aghiles Salah, Mohamed Nadif	In this paper, we aim to address the above issue and propose a new model which successfully integrates a word embedding model, word2vec, into an NMF framework so as to leverage the semantic relationships between words.
154	Term Proximity Constraints for Pseudo-Relevance Feedback	Ali Montazeralghaem, Hamed Zamani, Azadeh Shakery	In this paper, we propose three additional constraints based on the proximity of feedback terms to the query terms in the feedback documents. Previous work has introduced a set of constraints (axioms) that should be satisfied by any PRF model.
155	Gauging the Quality of Relevance Assessments using Inter-Rater Agreement	Tadele T. Damessie, Thao P. Nghiem, Falk Scholer, J. Shane Culpepper	In this work, we directly compare the reliability of judgments using three different types of bronze assessor groups.
156	Neural Citation Network for Context-Aware Citation Recommendation	Travis Ebesu, Yi Fang	We propose a flexible encoder-decoder architecture called Neural Citation Network (NCN), embodying a robust representation of the citation context with a max time delay neural network, further augmented with an attention mechanism and author networks.
157	A Study of SVM Kernel Functions for Sensitivity Classification Ensembles with POS Sequences	Graham McDonald, Nicolás García-Pedrajas, Craig Macdonald, Iadh Ounis	Therefore, in this work, we present an evaluation of five SVM kernel functions for sensitivity classification using POS sequences.
158	Entity Set Expansion via Knowledge Graphs	Xiangling Zhang, Yueguo Chen, Jun Chen, Xiaoyong Du, Ke Wang, Ji-Rong Wen	We propose a novel approach to solve the problem using knowledge graphs, by considering the deficiency (e.g., incompleteness) of knowledge graphs.
159	Word Embedding Causes Topic Shifting; Exploit Global Context!	Navid Rekabsaz, Mihai Lupu, Allan Hanbury, Hamed Zamani	To address this issue, we revisit the use of global context (i.e. the term co-occurrence in documents) to measure the term relatedness.
160	Building Your Own Reading List Anytime via Embedding Relevance, Quality, Timeliness and Diversity	Bo-Wen Zhang, Xu-Cheng Yin, Fang Zhou, Jian-Lin Jin	In this paper, we propose a searching framework for building a topical reading list anytime, where the Relevance (between topics and books), Quality (of books), Timeliness (of popularities) and Diversity (of results) are embedded into vector representations respectively based on user-generated contents and statistics on social media.
161	ENCORE: External Neural Constraints Regularized Distant Supervision for Relation Extraction	Siliang Tang, Jinjian Zhang, Ning Zhang, Fei Wu, Jun Xiao, Yueting Zhuang	In this paper, we proposed a novel neural framework, named ENCORE (External Neural COnstraints REgularized distant supervision), which allows an integration of other information for standard DS through regularizations under multiple external neural networks.
162	Information Retrieval Model using Generalized Pareto Distribution and Its Application to Instance Search	Masaya Murata, Kaoru Hiramatsu, Shin’ichi Satoh	We adopt the generalized Pareto distribution for the information-based model and show that the parameters can be estimated based on the mean excess function.
163	Predicting Information Seeking Intentions from Search Behaviors	Matthew Mitsui, Jiqun Liu, Nicholas J. Belkin, Chirag Shah	We present results of a study of 40 participants, each working on two different journalism tasks, which investigated how their search behaviors could indicate their intentions.
164	But Is It Statistically Significant?: Statistical Significance in IR Research, 1995-2014	Ben Carterette	We analyze 5,792 IR conference papers published over 20 years to investigate how researchers have used and are using statistical significance testing in their experiments
165	Generalized Mixed Effect Models for Personalizing Job Search	Ankan Saha, Dhruv Arya	We describe the details of the new method along with the challenges faced in launching such a model into production and making it efficient at a very large scale.
166	Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge	Arman Cohan, Nazli Goharian	To address this problem, we propose an unsupervised model that uses distributed representation of words as well as domain knowledge to extract the appropriate context from the reference paper.
167	Crowdsourced App Review Manipulation	Shanshan Li, James Caverlee, Wei Niu, Parisa Kaghazgaran	And yet, the user reviews and ratings on these marketplaces may be strategically targeted by app developers.
168	CitySearcher: A City Search Engine For Interests	Mohamed Abdel Maksoud, Gaurav Pandey, Shuaiqiang Wang	We introduce CitySearcher, a vertical search engine that searches for cities when queried for an interest. To reduce the effect of the mismatched semantic relationships, we generate a set of features for learning based on a novel clustering-based method.
169	Cross-Language Question Re-Ranking	Giovanni Da San Martino, Salvatore Romeo, Alberto Barroón-Cedeño, Shafiq Joty, Lluís Maàrquez, Alessandro Moschitti, Preslav Nakov	We observe that both approaches degrade the performance of the system when working on the translated text, especially the kernel-based system, which depends heavily on a syntactic kernel.
170	Open Relation Extraction for Support Passage Retrieval: Merit and Open Issues	Amina Kadry, Laura Dietz	Our goal is to complement an entity ranking with human-readable explanations of how those retrieved entities are connected to the information need.
171	Generating Query Suggestions to Support Task-Based Search	Dario Garigliotti, Krisztian Balog	We propose a probabilistic modeling framework that obtains keyphrases from multiple sources and generates query suggestions from these keyphrases.
172	De-duping URLs with Sequence-to-Sequence Neural Networks	Keyang Xu, Zhengzhong Liu, Jamie Callan	Traditional de-duping methods are usually limited to heavily engineered rule matching strategies.In this work, we propose a novel URL de-duping framework based on sequence-to-sequence (Seq2Seq) neural networks.
173	Graph Summarization for Entity Relatedness Visualization	Yukai Miao, Jianbin Qin, Wei Wang	In this work, we investigate how to summarize the relatedness graphs and how to use the summarized graphs to assistant the users to retrieve target information.
174	Layout and Semantics: Combining Representations for Mathematical Formula Search	Kenny Davila, Richard Zanibbi	We propose searching both formula representations using a three-layer model.
175	Understanding and Predicting Usefulness Judgment in Web Search	Jiaxin Mao, Yiqun Liu, Huanbo Luan, Min Zhang, Shaoping Ma, Hengliang Luo, Yuntao Zhang	Our study sheds light on the understanding of the dynamics of the user-perceived usefulness of documents in a search session and provides implications for the evaluation and design of Web search engines.
176	Open Source Repository Recommendation in Social Coding	Jyun-Yu Jiang, Pu-Jen Cheng, Wei Wang	The aim of this paper is to investigate the feasibility of leveraging user programming language preference to improve the performance of OCCF-based repository recommendation.
177	Venue Appropriateness Prediction for Personalized Context-Aware Venue Suggestion	Mohammad Aliannejadi, Fabio Crestani	In this paper, we present a set of novel scores to measure the similarity between a user and a candidate venue in a new city.
178	Building Bridges across Social Platforms: Answering Twitter Questions with Yahoo! Answers	Mossaab Bagdouri, Douglas W. Oard	This paper investigates techniques for answering microblog questions by searching in a large community question answering website.
179	Large-Scale Goodness Polarity Lexicons for Community Question Answering	Todor Mihaylov, Daniel Balchev, Yasen Kiprov, Ivan Koychev, Preslav Nakov	In particular, we use pointwise mutual information in order to build large-scale goodness polarity lexicons in a semi-supervised manner starting with a small number of initial seeds.
180	A Neural Language Model for Query Auto-Completion	Dae Hoon Park, Rikio Chiba	In order to suggest queries for previously unseen text, we propose a neural language model that learns how to generate a query from a starting text, a prefix.
181	Social Media Advertisement Outreach: Learning the Role of Aesthetics	Avikalp Srivastava, Madhav Datt, Jaikrishna Chaparala, Shubham Mangla, Priyadarshi Patnaik	Our paper is an initial study, where we propose a novel method to evaluate and improve outreach of promotional images from corporations on Twitter, based purely on their describable aesthetic attributes.
182	Seasonal Web Search Query Selection for Influenza-Like Illness (ILI) Estimation	Niels Dalum Hansen, Kåre Mølbak, Ingemar J. Cox, Christina Lioma	To address this issue, we propose modeling the seasonal variation in ILI activity and selecting queries that are correlated with the residual of the seasonal model and the observed ILI signal.
183	Improving Retrieval Performance for Verbose Queries via Axiomatic Analysis of Term Discrimination Heuristic	Mozhdeh Ariannezhad, Ali Montazeralghaem, Hamed Zamani, Azadeh Shakery	In this paper, we propose a constraint to model the interaction between query length and IDF.
184	Timestamping Entities using Contextual Information	Adam Jatowt, Daisuke Kawai, Katsumi Tanaka	We propose in this paper to estimate entity’s lifetimes using link structure in Wikipedia focusing on person entities.
185	On the Use of an Intermediate Class in Boolean Crowdsourced Relevance Annotations for Learning to Rank Comments	Alberto Barrón-Cedeño, Giovanni Da San Martino, Simone Filice, Alessandro Moschitti	Our main contribution is to show empirically that the inclusion of an intermediate class to assess Boolean relevance is not useful.
186	Embedding-based Query Expansion for Weighted Sequential Dependence Retrieval Model	Saeid Balaneshin-kordan, Alexander Kotov	In this paper, we propose Semantic Weighted Dependence Model (SWDM), a PRF based query expansion method for WSDM, which utilizes distributed low-dimensional word representations (i.e., word embeddings).
187	Experiments with Convolutional Neural Network Models for Answer Selection	Jinfeng Rao, Hua He, Jimmy Lin	This paper focuses on the problem of answer selection for question answering: we attempt to replicate the results of Severyn and Moschitti using their open-source code as well as to reproduce their results via a de novo (i.e., from scratch) implementation using a completely different deep learning toolkit.
188	Luandri: A Clean Lua Interface to the Indri Search Engine	Bhaskar Mitra, Fernando Diaz, Nick Craswell	To bridge this gap, we introduce Luandri (pronounced "laundry"), a simple interface for exposing the search capabilities of Indri to Torch models implemented in Lua.
189	Finally, a Downloadable Test Collection of Tweets	Royal Sequiera, Jimmy Lin	We analyzed both datasets in terms of content overlap and retrieval baselines to show that the Internet Archive data can serve as a drop-in replacement for the Tweets2013 collection, thereby providing the research community with, finally, a downloadable collection of tweets.
190	Cookpad Image Dataset: An Image Collection as Infrastructure for Food Research	Jun Harashima, Yuichiro Someya, Yohei Kikuta	In this study, we construct the Cookpad Image Dataset, a novel collection of food images taken from Cookpad, the largest recipe search service in the world.
191	SogouT-16: A New Web Corpus to Embrace IR Research	Cheng Luo, Yukun Zheng, Yiqun Liu, Xiaochuan Wang, Jingfang Xu, Min Zhang, Shaoping Ma	In this study, we present a Chinese Web collection, SogouT-16, which is the largest free-of-charge public Chinese Web collection so far.
192	A Test Collection for Evaluating Retrieval of Studies for Inclusion in Systematic Reviews	Harrisen Scells, Guido Zuccon, Bevan Koopman, Anthony Deacon, Leif Azzopardi, Shlomo Geva	This paper introduces a test collection for evaluating the effectiveness of different methods used to retrieve research studies for inclusion in systematic reviews.
193	One Million Posts: A Data Set of German Online Discussions	Dietmar Schabus, Marcin Skowron, Martin Trapp	In this paper we introduce a new data set consisting of user comments posted to the website of a German-language Austrian newspaper.
194	KASANDR: A Large-Scale Dataset with Implicit Feedback for Recommendation	Sumit Sidana, Charlotte Laclau, Massih R. Amini, Gilles Vandelle, André Bois-Crettez	In this paper, we describe a novel, publicly available collection for recommendation systems that records the behavior of customers of the European leader in eCommerce advertising, Kelkoo\footnote{\url{https://www.kelkoo.com/}}, during one month.
195	A Collection for Detecting Triggers of Sentiment Spikes	Anastasia Giachanou, Ida Mele, Fabio Crestani	With the aim to facilitate research on this problem, we describe a collection of tweets that can be used for detecting and ranking the likely triggers of sentiment spikes towards different entities.
196	Anserini: Enabling the Use of Lucene for Information Retrieval Research	Peilin Yang, Hui Fang, Jimmy Lin	This paper introduces Anserini, a new information retrieval toolkit that aims to provide the best of both worlds, to better align information retrieval practice and research.
197	A Stream-based Resource for Multi-Dimensional Evaluation of Recommender Algorithms	Benjamin Kille, Andreas Lommatzsch, Frank Hopfgartner, Martha Larson, Arjen P. de Vries	We introduce two resources supporting such evaluation methodologies: the new data set of stream recommendation interactions released for CLEF NewsREEL 2017, and the new Open Recommendation Platform (ORP).
198	A Large-Scale Query Spelling Correction Corpus	Matthias Hagen, Martin Potthast, Marcel Gohsen, Anja Rathgeber, Benno Stein	We present a new large-scale collection of 54,772 queries with manually annotated spelling corrections.
199	DBpedia-Entity v2: A Test Collection for Entity Search	Faegheh Hasibi, Fedor Nikolaev, Chenyan Xiong, Krisztian Balog, Svein Erik Bratsberg, Alexander Kotov, Jamie Callan	We develop and release a new version of this test collection, DBpedia-Entity v2, which uses a more recent DBpedia dump and a unified candidate result pool from the same set of retrieval models.
200	A Cross-Platform Collection for Contextual Suggestion	Mohammad Aliannejadi, Ida Mele, Fabio Crestani	In this paper, we release both collections that were used by the system above.
201	RELink: A Research Framework and Test Collection for Entity-Relationship Retrieval	Pedro Saleiro, Natasa Milic-Frayling, Eduarda Mendes Rodrigues, Carlos Soares	In this paper we describe a method for generating E-R test queries to support comprehensive E-R search experiments.
202	BioNex: A System For Biomedical News Event Exploration	Patrick Ernst, Arunav Mishra, Avishek Anand, Vinay Setty	We demonstrate BioNex, a system to mine, rank and visualize biomedical news events.
203	RankEval: An Evaluation and Analysis Framework for Learning-to-Rank Solutions	Claudio Lucchese, Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego, Salvatore Trani	In this demo paper we propose RankEval, an open-source tool for the analysis and evaluation of Learning-to-Rank (LtR) models based on ensembles of regression trees.
204	ReviewMiner: An Aspect-based Review Analytics System	Derek Wu, Hongning Wang	We develop an aspect-based sentiment analysis system named ReviewMiner.
205	Nordlys: A Toolkit for Entity-Oriented and Semantic Search	Faegheh Hasibi, Krisztian Balog, Darío Garigliotti, Shuo Zhang	We introduce Nordlys, a toolkit for entity-oriented and semantic search.
206	Teaching the Information Retrieval Process Using a Web-Based Environment and Game Mechanics	Thomas Wilhelm-Stein, Stefan Kahl, Maximilian Eibl	Teaching the Information Retrieval Process Using a Web-Based Environment and Game Mechanics
207	Smart Media Generation System for Broadcasting Contents	Jeong-Woo Son, Wonjoo Park, Sang-Yun Lee, Jinwoo Kim, Sun-Joong Kim	This paper proposes a new system for this purpose.
208	EvALL: Open Access Evaluation for Information Access Systems	Enrique Amigó, Jorge Carrillo-de-Albornoz, Mario Almagro-Cádiz, Julio Gonzalo, Javier Rodríguez-Vidal, Felisa Verdejo	The EvALL online evaluation service aims to provide a unified evaluation framework for Information Access systems that makes results completely comparable and publicly available for the whole research community.
209	TOTEM: Personal Tweets Summarization on Mobile Devices	Jin Yao Chin, Sourav S. Bhowmick, Adam Jatowt	Given that 80% of active Twitter users access the site on mobile devices, in this demonstration we present a lightweight, personalized, on-demand, topic modeling-based tweets summarization engine called TOTEM, designed for such devices.
210	LSTM vs. BM25 for Open-domain QA: A Hands-on Comparison of Effectiveness and Efficiency	Sosuke Kato, Riku Togashi, Hideyuki Maeda, Sumio Fujita, Tetsuya Sakai	In this demonstration, we provide the attendees of SIGIR 2017 an opportunity to experience a live comparison of two open-domain QA systems, one based on a long short-term memory (LSTM) architecture with over 11 million Yahoo! Chiebukuro (i.e., Japanese Yahoo! Answers) questions and over 27.4 million answers for training, and the other based on BM25.
211	Proactive Information Retrieval via Screen Surveillance	Tung Vuong, Giulio Jacucci, Tuukka Ruotsalo	We demonstrate proactive information retrieval via screen surveillance.
212	ASTERIX: Ambiguity and Missing Element-Aware XML Keyword Search Engine	Ba Quan Truong, Sourav S. Bhowmick, Curtis Dyreson, Hong Jing Khok	We demonstrate ASTERIX, an innovative XKS engine that addresses these limitations.
213	Visual Pool: A Tool to Visualize and Interact with the Pooling Method	Aldo Lipani, Mihai Lupu, Allan Hanbury	In this paper we develop a novel visualization technique for the pooling method, and integrate it in a demo application named Visual Pool.
214	Event Detection on Curated Tweet Streams	Nimesh Ghelani, Salman Mohammed, Shine Wang, Jimmy Lin	We present a system for identifying interesting social media posts on Twitter and delivering them to users’ mobile devices in real time as push notifications.
215	A Task-oriented Search Engine for Evidence-based Medicine	Bevan Koopman, Guido Zuccon, Jack Russell	This paper describes a search engine specifically designed for searching medical literature for the purpose of EBM and in a clinical decision support setting.
216	Social Media Image Recognition for Food Trend Analysis	Giuseppe Amato, Paolo Bolettieri, Vinicius Monteiro de Lira, Cristina Ioana Muntean, Raffaele Perego, Chiara Renso	The system that we propose, WorldFoodMap, captures the stream of food photos from social media and, thanks to a CNN food image classifier, identifies the categories of food that people are sharing.
217	Computing Web-scale Topic Models using an Asynchronous Parameter Server	Rolf Jagerman, Carsten Eickhoff, Maarten de Rijke	We present APS-LDA, which integrates state-of-the-art topic modeling with cluster computing frameworks such as Spark using a novel asynchronous parameter server.
218	Seeing Bot	Yingwei Pan, Zhaofan Qiu, Ting Yao, Houqiang Li, Tao Mei	We demonstrate a video captioning bot, named Seeing Bot, which can generate a natural language description about what it is seeing in near real time.
219	MAPping the probability of start-up success	David Hawking	It turned out that end-users cared about search relevance only to the extent of reaching for buckets of vitriol when search failed to find what they wanted, and people making purchasing decisions were more interested in other things: what repositories can be included in the search, how responsive search is to updates, what security models are provided, the appearance of the search pages, and achieving internal business goals — even at the expense of end-user needs.
220	Search Without a Query: Powering Job Recommendations via Search Index at LinkedIn	Dhruv Arya, Ganesh Venkataraman	In this paper, we describe how the jobs recommendations is powered by a search index and some practical challenges involved in scaling such a system.
221	Spotify: Music Access At Scale	Fernando Diaz	In this presentation, we will highlight the research involved in developing Spotify and outline a research program for large scale music access.
222	Structuring the Unstructured: From Startup to Making Sense of eBay’s Huge eCommerce Inventory	Ido Guy, Kira Radinsky	In this proposed presentation, we will share the story of a research startup from its inception until its acquisition and integration as eBay’s data science team.
223	Making Ads More Relevant Innovations in Digital Advertising	Sudong Chung	In this talk, I will talk about the history of digital advertising and the recent innovations in advertising technologies to make ads more relevant to individual viewer.
224	Traditional IR Meets Ontology Engineering in Search for Data	Anton Firsov	Traditional IR Meets Ontology Engineering in Search for Data
225	Semantic Query Understanding	Ricardo Baeza-Yates	To accomplish semantic ranking, we use machine learning in several stages.
226	Twicalli: An Earthquake Detection System Based on Citizen Sensors Used for Emergency Response in Chile	Barbara Poblete	In this talk I will describe "Twicalli", a real-time earthquake detection system based on citizen sensors.
227	Cross-Lingual Information Retrieve in Sogou Search	Jingfang Xu, Feifei Zhai, Zhengshan Xue	Cross-Lingual Information Retrieve in Sogou Search
228	Find Shoes Like These	Hideyuki Maeda	We present an Euclidean embedding image representation, which serves to rank auction item images through wide range of semantic similarity spectrum, in the order of the relevance to the given query image much more effective than the baseline method in terms of a graded relevance measure.
229	Machine Learning Powered A/B Testing	Pavel Serdyukov	So, we proposed several metrics that learn the models of the trend in such time series and use them to quantify the changes in the user behavior.
230	Naver Search: Deep Learning Powered Search Portal for Intelligent Information Provision	Inho Kang	In this talk, I’ll cover some efforts and challenges in understanding and satisfying users on various devices.
231	Managing Tail Latencies in Large Scale IR Systems	Joel Mackenzie	In the proposed PhD project, we focus on improving the efficiency of high percentile tail latencies in large scale IR systems while minimising end-to-end effectiveness loss.
232	Health Misinformation in Search and Social Media	Amira Ghenai	In this paper, I briefly discuss my current work including background key references.
233	Relevance Judgments: Preferences, Scores and Ties	Ziying Yang	In order to have a better understanding of users’ perceptions of relevance and collect data with high fidelity, we propose to use the Pairwise Preference technique [2] to collect relevance judgments from a crowdsourcing platform.
234	Searchbots: Using Chatbots in Collaborative Information-seeking Tasks	Sandeep Avula	RG1: Our first research goal will be to investigate the use of searchbots in a collaborative search scenario.
235	Examining Information on Social Media: Topic Modelling, Trend Prediction and Community Classification	Anjie Fang	The second task is to propose a topic modelling approach that generates more coherent topics for social media data.
236	Dynamic Personalized Ranking of Facets for Exploratory Search	Esraa Ali	I am proposing a personalized approach to the dynamic ranking of facets.
237	Multi-dimensional Formula Feature Modeling for Mathematical Information Retrieval	Ke Yuan	In this study, I propose a novel formula feature modeling method for mathematical information retrieval.
238	Deep Collaborative Filtering Approaches for Context-Aware Venue Recommendation	Jarana Manotumruksa	Deep Collaborative Filtering Approaches for Context-Aware Venue Recommendation
239	Utilizing Online Social Media for Disaster Relief: Practical Challenges in Retrieval	Moumita Basu	Utilizing Online Social Media for Disaster Relief: Practical Challenges in Retrieval
240	Statistical Significance Testing in Information Retrieval: Theory and Practice	Ben Carterette	Statistical Significance Testing in Information Retrieval: Theory and Practice
241	Candidate Selection for Large Scale Personalized Search and Recommender Systems	Dhruv Arya, Ganesh Venkataraman, Aman Grover, Krishnaram Kenthapadi	In this tutorial we survey various candidate selection techniques and deep dive into case studies on a large scale social media platform.
242	A/B Testing at Scale: Accelerating Software Innovation	Alex Deng, Pavel Dmitriev, Somit Gupta, Ron Kohavi, Paul Raff, Lukas Vermeer	In this tutorial we will give an introduction to A/B testing, share key lessons learned from scaling experimentation at Bing to thousands of experiments per year, present real examples, and outline promising directions for future work.
243	Probabilistic Topic Models for Text Data Retrieval and Analysis	ChengXiang Zhai	This tutorial will systematically review the major research progress in probabilistic topic models and discuss their applications in text retrieval and text mining.
244	Neural Networks for Information Retrieval	Tom Kenter, Alexey Borisov, Christophe Van Gysel, Mostafa Dehghani, Maarten de Rijke, Bhaskar Mitra	The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they benefit IR research.
245	Building Test Collections: An Interactive Guide for Students and Others Without Their Own Evaluation Conference Series	Ian Soboroff	The goal of this tutorial is to lay out issues, procedures, pitfalls, and practical advice.
246	From Design to Analysis: Conducting Controlled Laboratory Experiments with Users	Diane Kelly, Anita Crescenzi	The goals of the tutorial are to increase participants’ (1) understanding of the uses of controlled laboratory experiments with human participants; (2) understanding of the technical vocabulary and procedures associated with such experiments and (3) confidence in conducting and evaluating IIR experiments.
247	SIGIR 2017 Tutorial on Health Search (HS2017): A Full-day from Consumers to Clinicians	Guido Zuccon, Bevan Koopman	The HS2017 tutorial will cover topics from an area of information retrieval (IR) with significant societal impact – health search.
248	Axiomatic Thinking for Information Retrieval: And Related Tasks	Enrique Amigo, Hui Fang, Stefano Mizzaro, ChengXiang Zhai	The workshop aims to help foster collaboration of researchers working on different perspectives of axiomatic thinking and encourage discussion and research on general methodological issues related to applying axiomatic thinking to IR and related tasks.
249	Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2017)	Muthu Kumar Chandrasekaran, Kokil Jaidka, Philipp Mayr	Bibliometrics, information retrieval (IR), text mining and NLP techniques could help in these search and look-up activities, but are not yet widely used.
250	First International Workshop on Conversational Approaches to Information Retrieval (CAIR’17)	Hideo Joho, Lawrence Cavedon, Jaime Arguello, Milad Shokouhi, Filip Radlinski	A specific focus is on techniques that support complex and multi-turn user-machine dialogues for information access and retrieval, and multi-model interfaces for interacting with such systems.
251	SIGIR 2017 Workshop on eCommerce (ECOM17)	Jon Degenhardt, Surya Kallumadi, Maarten de Rijke, Luo Si, Andrew Trotman, Yinghui Xu	SIGIR 2017 Workshop on eCommerce (ECOM17)
252	The First Workshop on Knowledge Graphs and Semantics for Text Retrieval and Analysis (KG4IR)	Laura Dietz, Chenyan Xiong, Edgar Meij	The goal of this workshop is to bring together a community of researchers and practitioners who are interested in using, aligning, and constructing knowledge graphs and similar semantic resources for information retrieval applications.
253	The Lucene for Information Access and Retrieval Research (LIARR) Workshop at SIGIR 2017	Leif Azzopardi, Matt Crane, Hui Fang, Grant Ingersoll, Jimmy Lin, Yashar Moshfeghi, Harrisen Scells, Peilin Yang, Guido Zuccon	Our goal is to promote the use of Lucene for information access and retrieval research.
254	SIGIR 2017 Workshop on Neural Information Retrieval (Neu-IR’17)	Nick Craswell, W Bruce Croft, Maarten de Rijke, Jiafeng Guo, Bhaskar Mitra	After the first successful Neu-IR workshop at SIGIR 2016, our goal this year will be to host a highly interactive full-day workshop to bring the neural IR community together to specifically address these key challenges facing this line of research.
255	SIGIR 2017 Workshop on Open Knowledge Base and Question Answering (OKBQA2017)	Key-Sun Choi, Teruko Mitamura, Piek Vossen, Jin-Dong Kim, Axel-Cyrille Ngonga Ngomo	SIGIR 2017 Workshop on Open Knowledge Base and Question Answering (OKBQA2017)