Hit enter to search or ESC to close

Paper Digest: SIGIR 2018 Highlights

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.

If you do not want to miss any interesting academic paper, you are welcome to sign up our free daily paper digest service to get updates on new papers published in your area every day. You are also welcome to follow us on Twitter and Linkedin to get updated with new conference digests.

Paper Digest Team
team@paperdigest.org

TABLE 1: SIGIR 2018 Papers

Title Authors Highlight
1 Salton Award Keynote: Information Interaction in Context Kalervo P. Jarvelin Salton Award Keynote: Information Interaction in Context
2 Data Science for Social Good and Public Policy: Examples, Opportunities, and Challenges Rayid Ghani In this talk, I’ll discuss lessons learned from our work at University of Chicago while working on dozens of data science projects over the past few years with non-profits and governments on high-impact public policy and social challenges in criminal justice, public health, education, economic development, public safety, workforce training, and urban infrastructure.
3 Neural Compatibility Modeling with Attentive Knowledge Distillation Xuemeng Song, Fuli Feng, Xianjing Han, Xin Yang, Wei Liu, Liqiang Nie Towards this end, in this work, we shed light on the complementary clothing matching by integrating the advanced deep neural networks and the rich fashion domain knowledge.
4 Attentive Moment Retrieval in Videos Meng Liu, Xiang Wang, Liqiang Nie, Xiangnan He, Baoquan Chen, Tat-Seng Chua We evaluate our method on two datasets: DiDeMo and TACoS.
5 Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach Chuan Qin, Hengshu Zhu, Tong Xu, Chen Zhu, Liang Jiang, Enhong Chen, Hui Xiong To this end, in this paper, we propose a novel end-to-end A bility-aware P erson-J ob F it N eural N etwork (APJFNN) model, which has a goal of reducing the dependence on manual labour and can provide better interpretation about the fitting results.
6 Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings Micael Carvalho, Rémi Cadène, David Picard, Laure Soulier, Nicolas Thome, Matthieu Cord In this paper, we propose a cross-modal retrieval model aligning visual and textual data (like pictures of dishes and their recipes) in a shared representation space.
7 A Click Sequence Model for Web Search Alexey Borisov, Martijn Wardenaar, Ilya Markov, Maarten de Rijke In this paper, we for the first time focus on modeling and predicting sequences of interaction events.
8 Understanding and Evaluating User Satisfaction with Music Discovery Jean Garcia-Gathright, Brian St. Thomas, Christine Hosey, Zahra Nazari, Fernando Diaz We adopt a mixed methods approach, including interviews, unsupervised learning, survey research, and statistical modeling, to understand and evaluate user satisfaction in the context of discovery.
9 Identifying Users behind Shared Accounts in Online Streaming Services Jyun-Yu Jiang, Cheng-Te Li, Yian Chen, Wei Wang Our goal is three-fold: (1) Given an account, along with its historical session logs, we identify a set of users who share such account; (2) Given a new session issued by an account, we find the corresponding user among the identified users of such account; (3) We aim to boost the performance of item recommendation by user identification.
10 Predicting User Knowledge Gain in Informational Search Sessions Ran Yu, Ujwal Gadiraju, Peter Holtz, Markus Rokicki, Philipp Kemkes, Stefan Dietze In this paper, we introduce a supervised model to predict a user’s knowledge state and knowledge gain from features captured during the search sessions.
11 Dynamic Shard Cutoff Prediction for Selective Search Hafeezul Rahman Mohammad, Keyang Xu, Jamie Callan, J. Shane Culpepper A new algorithm is presented that predicts the number of shards that must be searched for a given query in order to meet recall-oriented goals.
12 Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks Guokun Lai, Wei-Cheng Chang, Yiming Yang, Hanxiao Liu In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge.
13 Neural Query Performance Prediction using Weak Supervision from Multiple Signals Hamed Zamani, W. Bruce Croft, J. Shane Culpepper In this paper, we propose a general end-to-end query performance prediction framework based on neural networks, called NeuralQPP.
14 Combined Regression and Tripletwise Learning for Conversion Rate Prediction in Real-Time Bidding Advertising Lili Shan, Lei Lin, Chengjie Sun This paper is focused on the CVR estimation problem for buy-sides in RTB and a combined regression and tripletwise ranking method (CRT) is proposed that jointly considers regression loss and tripletwise ranking loss to estimate the CVR.
15 From Greedy Selection to Exploratory Decision-Making: Diverse Ranking with Policy-Value Networks Yue Feng, Jun Xu, Yanyan Lan, Jiafeng Guo, Wei Zeng, Xueqi Cheng In this paper we propose to partially alleviate the problem with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP), referred to as M$^2$Div.
16 Learning a Deep Listwise Context Model for Ranking Refinement Qingyao Ai, Keping Bi, Jiafeng Guo, W. Bruce Croft Inspired by the idea of pseudo relevance feedback where top ranked documents, which we refer as the local ranking context, can provide important information about the query’s characteristics, we propose to use the inherent feature distributions of the top results to learn a Deep Listwise Context Model that helps us fine tune the initial ranked list.
17 Efficient Exploration of Gradient Space for Online Learning to Rank Huazheng Wang, Ramsey Langley, Sonwoo Kim, Eric McCord-Snook, Hongning Wang In this paper, we accelerate the online learning process by efficient exploration in the gradient space.
18 Selective Gradient Boosting for Effective Learning to Rank Claudio Lucchese, Franco Maria Nardini, Raffaele Perego, Salvatore Orlando, Salvatore Trani In this paper, we propose Selective Gradient Boosting (SelGB), an algorithm addressing the Learning-to-Rank task by focusing on those irrelevant documents that are most likely to be mis-ranked, thus severely hindering the quality of the learned model.
19 Explainable Recommendation via Multi-Task Learning in Opinionated Text Data Nan Wang, Hongning Wang, Yiling Jia, Yue Yin In this work, we develop a multi-task learning solution for explainable recommendation.
20 Sentiment Analysis of Peer Review Texts for Scholarly Papers Ke Wang, Xiaojun Wan We propose a multiple instance learning network with a novel abstract-based memory mechanism (MILAM) to address this challenging task.
21 Attentive Recurrent Social Recommendation Peijie Sun, Le Wu, Meng Wang To this end, in this paper, we present an attentive recurrent network based approach for temporal social recommendation.
22 Mention Recommendation for Multimodal Microblog with Cross-attention Memory Network Renfeng Ma, Qi Zhang, Jiawen Wang, Lizhen Cui, Xuanjing Huang To make full use of textual and visual information, we propose a novel cross-attention memory network to perform the mention recommendation task for multimodal tweets.
23 Learning Geo-Social User Topical Profiles with Bayesian Hierarchical User Factorization Haokai Lu, Wei Niu, James Caverlee To capture the pair-wise interactions between geo-location and user’s topical profile in social-spatial systems, we propose the modeling of fine-grained and multi-dimensional user geo-topic profiles.
24 Target Apps Selection: Towards a Unified Search Framework for Mobile Devices Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, W. Bruce Croft In this paper, we take the first step forward towards developing unified mobile search.
25 Dialogue Act Recognition via CRF-Attentive Structured Network Zheqian Chen, Rongqin Yang, Zhou Zhao, Deng Cai, Xiaofei He In this paper, we tackle the problem of DAR from the viewpoint of extending richer Conditional Random Field (CRF) structured dependencies without abandoning end-to-end training.
26 Conversational Recommender System Yueming Sun, Yi Zhang In this work, we propose to integrate research in dialog systems and recommender systems into a novel and unified deep reinforcement learning framework to build a personalized conversational recommendation agent that optimizes a per session based utility function. We propose a set of machine actions tailored for recommendation agents and train a deep policy network to decide which action (i.e. asking for the value of a facet or making a recommendation) the agent should take at each step.
27 Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems Liu Yang, Minghui Qiu, Chen Qu, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Jun Huang, Haiqing Chen In this paper, we propose a learning framework on the top of deep neural matching networks that leverages external knowledge for response ranking in information-seeking conversation systems.
28 Chat More: Deepening and Widening the Chatting Topic via A Deep Model Wenjie Wang, Minlie Huang, Xin-Shun Xu, Fumin Shen, Liqiang Nie In this paper, we study the task of response generation in open-domain multi-turn dialog systems.
29 Identifying Sub-events and Summarizing Disaster-Related Information from Microblogs Koustav Rudra, Pawan Goyal, Niloy Ganguly, Prasenjit Mitra, Muhammad Imran This research proposes a novel approach to help crisis responders fulfill their information needs at different levels of granularities.
30 The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News Nguyen Vo, Kyumin Lee To fill this gap, in this paper, we (i) collect and analyze online users called guardians, who correct misinformation and fake news in online discussions by referring fact-checking URLs; and (ii) propose a novel fact-checking URL recommendation model to encourage the guardians to engage more in fact-checking activities.
31 Intent-aware Query Obfuscation for Privacy Protection in Personalized Web Search Wasi Uddin Ahmad, Kai-Wei Chang, Hongning Wang In this work, we propose a client-centered intent-aware query obfuscation solution for protecting user privacy in a personalized web search scenario.
32 A Personal Privacy Preserving Framework: I Let You Know Who Can See What Xuemeng Song, Xiang Wang, Liqiang Nie, Xiangnan He, Zhumin Chen, Wei Liu In this paper, we aim to reduce users’ privacy risks on social networks by answering the question of Who Can See What. In particular, we first collect a set of posts and extract a group of privacy-oriented features to describe the posts.
33 SynTF: Synthetic and Differentially Private Term Frequency Vectors for Privacy-Preserving Text Mining Benjamin Weggenmann, Florian Kerschbaum We therefore propose an automated text anonymization approach that produces synthetic term frequency vectors for the input documents that can be used in lieu of the original vectors.
34 Privacy-aware Ranking with Tree Ensembles on the Cloud Shiyu Ji, Jinjin Shao, Daniel Agun, Tao Yang To address privacy with tree-based server-side ranking, this paper proposes to reduce the learning-to-rank model dependence on composite features as a trade-off, and develops comparison-preserving mapping to hide feature values and tree thresholds.
35 Multihop Attention Networks for Question Answer Matching Nam Khanh Tran, Claudia Niedereée In this paper, we propose Multihop Attention Networks (MAN) which aim to uncover these complex relations for ranking question and answer pairs.
36 Ranking Documents by Answer-Passage Quality Evi Yulianti, Ruey-Cheng Chen, Falk Scholer, W. Bruce Croft, Mark Sanderson Based on a novel use of Community Question Answering data, we present an approach for the creation of such passages.
37 Characterizing and Supporting Question Answering in Human-to-Human Communication Xiao Yang, Ahmed Hassan Awadallah, Madian Khabsa, Wei Wang, Miaosen Wang In this paper, we focus on information exchange over enterprise email in the form of questions and answers.
38 Adversarial Personalized Ranking for Recommendation Xiangnan He, Zhankui He, Xiaoyu Du, Tat-Seng Chua To enhance the robustness of a recommender model and thus improve its generalization performance, we propose a new optimization framework, namely Adversarial Personalized Ranking (APR).
39 Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce Liang Wu, Diane Hu, Liangjie Hong, Huan Liu In this paper, we address these differences with a novel learning framework for EC product search called LETORIF (LEarning TO Rank with Implicit Feedback).
40 Modeling Diverse Relevance Patterns in Ad-hoc Retrieval Yixing Fan, Jiafeng Guo, Yanyan Lan, Jun Xu, Chengxiang Zhai, Xueqi Cheng In this work, we propose a data-driven method to allow relevance signals at different granularities to compete with each other for final relevance assessment.
41 Unbiased Learning to Rank with Unbiased Propensity Estimation Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft In this work, we address those problems by unifying the learning of propensity models and ranking models.
42 Ranking Robustness Under Adversarial Document Manipulations Gregory Goren, Oren Kurland, Moshe Tennenholtz, Fiana Raiber We propose several measures for quantifying ranking robustness and use them to analyze ranking competitions between documents’ authors.
43 Equity of Attention: Amortizing Individual Fairness in Rankings Asia J. Biega, Krishna P. Gummadi, Gerhard Weikum This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias which leads to disproportionately less attention being paid to low-ranked subjects.
44 Should I Follow the Crowd?: A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems Rocío Cañamares, Pablo Castells Recent research has confirmed and measured such biases, and proposed methods to avoid them. We build a crowdsourced dataset devoid of the usual biases displayed by common publicly available data, in which we illustrate contradictions between the accuracy that would be measured in a common biased offline experimental setting, and the actual accuracy that can be measured with unbiased observations.
45 Constructing an Interaction Behavior Model for Web Image Search Xiaohui Xie, Jiaxin Mao, Maarten de Rijke, Ruizhe Zhang, Min Zhang, Shaoping Ma In this paper, we conduct a comprehensive image search user behavior analysis using data from a lab-based user study as well as data from a commercial search log.
46 Between Clicks and Satisfaction: Study on Multi-Phase User Preferences and Satisfaction for Online News Reading Hongyu Lu, Min Zhang, Shaoping Ma Besides, we find that user preference changes frequently, hence preferences in three phases are proposed: Before-Read Preference, After-Read Preference and Post-task Preference.
47 The Effects of Manipulating Task Determinability on Search Behaviors and Outcomes Robert Capra, Jaime Arguello, Heather O’Brien, Yuan Li, Bogeum Choi In this work, we manipulated the determinability of comparative tasks.
48 Seed-driven Document Ranking for Systematic Reviews in Evidence-Based Medicine Grace E. Lee, Aixin Sun In this paper, we propose a seed-driven document ranking (SDR) model for effective screening, with the assumption that one relevant document is known, i.e., the seed document.
49 A Dataset and an Examination of Identifying Passages for Due Diligence Adam Roegiest, Alexander K. Hudek, Anne McNulty We present and formalize the due diligence problem, where lawyers extract data from legal documents to assess risk in a potential merger or acquisition, as an information retrieval task.
50 Generating Better Queries for Systematic Reviews Harrisen Scells, Guido Zuccon In this paper we investigate whether a better Boolean query than that defined in the protocol of a systematic review, can be created, and we develop methods for the transformation of a given Boolean query into a more effective one.
51 Modeling Dynamic Pairwise Attention for Crime Classification over Legal Articles Pengfei Wang, Ze Yang, Shuzi Niu, Yongfeng Zhang, Lei Zhang, ShaoZhang Niu In this paper, we focus on how to save the manual efforts and make the conviction process more efficient.
52 Learning Contextual Bandits in a Non-stationary Environment Qingyun Wu, Naveen Iyer, Hongning Wang In this paper, we consider the situation where the underlying distribution of reward remains unchanged over (possibly short) epochs and shifts at unknown time instants.
53 Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, Edward Y. Chang To address these issues, in this paper, we propose a novel knowledge enhanced sequential recommender.
54 Collaborative Memory Network for Recommendation Systems Travis Ebesu, Bin Shen, Yi Fang We propose Collaborative Memory Networks (CMN), a deep architecture to unify the two classes of CF models capitalizing on the strengths of the global structure of latent factor model and local neighborhood-based structure in a nonlinear fashion.
55 Streaming Ranking Based Recommender Systems Weiqing Wang, Hongzhi Yin, Zi Huang, Qinyong Wang, Xingzhong Du, Quoc Viet Hung Nguyen In this paper, we investigate the problem of streaming recommendations being subject to higher input rates than they can immediately process with their available system resources (i.e., CPU and memory).
56 Torch: A Search Engine for Trajectory Data Sheng Wang, Zhifeng Bao, J. Shane Culpepper, Zizhe Xie, Qizhi Liu, Xiaolin Qin This paper presents a new trajectory search engine called Torch for querying road network trajectory data.
57 Top-k Route Search through Submodularity Modeling of Recurrent POI Features Hongwei Liang, Ke Wang We model the personalized diversity requirement by the whole class of submodular functions, and present an optimal solution to the top-k route search problem through indices for retrieving relevant POIs in both feature and route spaces and various strategies for pruning the search space using user preferences and constraints.
58 A Contextual Attention Recurrent Architecture for Context-Aware Venue Recommendation Jarana Manotumruksa, Craig Macdonald, Iadh Ounis To address these challenges, we propose a novel Contextual Attention Recurrent Architecture (CARA) that leverages both sequences of feedback and contextual information associated with the sequences to capture the users’ dynamic preferences.
59 Entity Set Search of Scientific Literature: An Unsupervised Ranking Approach Jiaming Shen, Jinfeng Xiao, Xinwei He, Jingbo Shang, Saurabh Sinha, Jiawei Han To address these challenges, we introduce SetRank, an unsupervised ranking framework that models inter-entity relationships and captures entity type information.
60 Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling Chenyan Xiong, Zhengzhong Liu, Jamie Callan, Tie-Yan Liu This paper presents a Kernel Entity Salience Model (KESM) that improves text understanding and retrieval by better estimating entity salience (importance) in documents.
61 Automated Comparative Table Generation for Facilitating Human Intervention in Multi-Entity Resolution Jiacheng Huang, Wei Hu, Haoxuan Li, Yuzhong Qu In this paper, we propose an automated approach to select a few important properties and values for a set of entities, and assemble them by a comparative table.
62 On-the-fly Table Generation Shuo Zhang, Krisztian Balog Unlike previous work, which is limited to retrieving existing tables, we aim to answer queries by automatically compiling a table in response to a query.
63 Measuring the Utility of Search Engine Result Pages: An Information Foraging Based Measure Leif Azzopardi, Paul Thomas, Nick Craswell In this paper, we aim to measure the utility and cost of SERPs.
64 How Well do Offline and Online Evaluation Metrics Measure User Satisfaction in Web Image Search? Fan Zhang, Ke Zhou, Yunqiu Shao, Cheng Luo, Min Zhang, Shaoping Ma To shed light on this, we conduct a laboratory user study that collects both explicit user satisfaction feedbacks as well as user behavior signals such as clicks.
65 An Axiomatic Analysis of Diversity Evaluation Metrics: Introducing the Rank-Biased Utility Metric Enrique Amigó, Damiano Spina, Jorge Carrillo-de-Albornoz In this paper, we define a constraint-based axiomatic framework to study the suitability of existing metrics in search result diversification scenarios.
66 Cross-language Citation Recommendation via Hierarchical Representation Learning on Heterogeneous Graph Zhuoren Jiang, Yue Yin, Liangcai Gao, Yao Lu, Xiaozhong Liu In this study, we propose a novel solution, cross-language citation recommendation via Hierarchical Representation Learning on Heterogeneous Graph (HRLHG), to address this new problem.
67 Attentive Group Recommendation Da Cao, Xiangnan He, Lianhai Miao, Yahui An, Chao Yang, Richang Hong Toward this end, we contribute a novel solution, namely AGREE (short for ”Attentive Group REcommEndation”), to address the preference aggregation problem by learning the aggregation strategy from data, which is based on the recent developments of attention network and neural collaborative filtering (NCF).
68 Calendar-Aware Proactive Email Recommendation Qian Zhao, Paul N. Bennett, Adam Fourney, Anne Loomis Thompson, Shane Williams, Adam D. Troy, Susan T. Dumais In this paper, we study how to leverage calendar information to help with email re-finding using a zero-query prototype, Calendar-Aware Proactive Email Recommender System (CAPERS).
69 Structuring Wikipedia Articles with Section Recommendations Tiziano Piccardi, Michele Catasta, Leila Zia, Robert West Inspired by this need, the present paper defines the problem of section recommendation for Wikipedia articles and proposes several approaches for tackling it.
70 On Fine-Grained Relevance Scales Kevin Roitero, Eddy Maddalena, Gianluca Demartini, Stefano Mizzaro In this work, we propose and experimentally evaluate a bounded and fine-grained relevance scale having many of the advantages and dealing with some of the issues of ME.
71 Automatic Ground Truth Expansion for Timeline Evaluation Richard McCreadie, Craig Macdonald, Iadh Ounis We show that when considering lower-effectiveness systems, the test collections are robust (the likelihood of systems being miss-ranked is low).
72 Stochastic Simulation of Test Collections: Evaluation Scores Julián Urbano, Thomas Nagler We provide a full R package to simulate new data following the proposed method, which can also be used to fully reproduce the results in this paper.
73 Offline Comparative Evaluation with Incremental, Minimally-Invasive Online Feedback Ben Carterette, Praveen Chandar We present a methodology for incrementally logging interactions on previously-unseen documents for use in computation of an unbiased estimator of a new ranker’s effectiveness.
74 BiNE: Bipartite Network Embedding Ming Gao, Leihui Chen, Xiangnan He, Aoying Zhou In this paper, we propose a new method named BiNE, short for Bipartite Network Embedding, to learn the vertex representations for bipartite networks.
75 Deep Domain Adaptation Hashing with Adversarial Learning Fuchen Long, Ting Yao, Qi Dai, Xinmei Tian, Jiebo Luo, Tao Mei In the paper, we facilitate this issue in an adversarial learning framework, in which a domain discriminator is devised to handle domain shift.
76 Fast Scalable Supervised Hashing Xin Luo, Liqiang Nie, Xiangnan He, Ye Wu, Zhen-Duo Chen, Xin-Shun Xu In this paper, we present a novel supervised hashing method, called Fast Scalable Supervised Hashing (FSSH), which circumvents the use of the large similarity matrix by introducing a pre-computed intermediate term whose size is independent with the size of training data.
77 Enriching Taxonomies With Functional Domain Knowledge Nikhita Vedula, Patrick K. Nicholson, Deepak Ajwani, Sourav Dutta, Alessandra Sala, Srinivasan Parthasarathy To this end we propose a novel framework, ETF, to enrich large-scale, generic taxonomies with new concepts from resources such as news and research publications.
78 On Link Prediction in Knowledge Bases: Max-K Criterion and Prediction Protocols Jiajie Mei, Richong Zhang, Yongyi Mao, Ting Deng This paper introduces a new criterion, referred to as max-k.
79 Weakly-supervised Contextualization of Knowledge Graph Facts Nikos Voskarides, Edgar Meij, Ridho Reinanda, Abhinav Khaitan, Miles Osborne, Giorgio Stefanoni, Prabhanjan Kambadur, Maarten de Rijke We introduce a neural fact contextualization method (\em NFCM ) to address the KG fact contextualization task. In order to obtain the annotations required to train the learning to rank model at scale, we generate training data automatically using distant supervision on a large entity-tagged text corpus.
80 Constructing Click Models for Mobile Search Jiaxin Mao, Cheng Luo, Min Zhang, Shaoping Ma To address this problem, we propose a novel Mobile Click Model (MCM) that models how users examine and click search results on mobile SERPs.
81 Update Delivery Mechanisms for Prospective Information Needs: An Analysis of Attention in Mobile Users Jimmy Lin, Salman Mohammed, Royal Sequiera, Luchen Tan We refer to these mechanisms as push-based vs. pull-based, and present a two-year contrastive study that attempts to understand the effects of the delivery mechanism on mobile user behavior, in the context of the TREC Real-Time Summarization Tracks.
82 Item Retrieval as Utility Estimation Shawn R. Wolfe, Yi Zhang Towards this end, we present a retrieval model inspired by multi-criteria decision making theory, concentrating on numeric attributes.
83 Crowd vs. Expert: What Can Relevance Judgment Rationales Teach Us About Assessor Disagreement? Mucahid Kutlu, Tyler McDonnell, Yassmine Barkallah, Tamer Elsayed, Matthew Lease In this work, we scale up our rationale-based judging design to assess its reliability on the 2014 TREC Web Track, collecting roughly 25K crowd judgments for 5K document-topic pairs.
84 CAN: Enhancing Sentence Similarity Modeling with Collaborative and Adversarial Network Qin Chen, Qinmin Hu, Jimmy Xiangji Huang, Liang He In this paper, we propose a Collaborative and Adversarial Network (CAN), which explicitly models the common features between two sentences for enhancing sentence similarity modeling.
85 Identify Shifts of Word Semantics through Bayesian Surprise Zhuofeng Wu, Cheng Li, Zhe Zhao, Fei Wu, Qiaozhu Mei In this paper, we present a novel computational method to capture such changes and to model the evolution of word semantics.
86 From Royals to Vegans: Characterizing Question Trolling on a Community Question Answering Website Ido Guy, Bracha Shapira In this paper, we study a particular type of trolling, performed by asking a provocative question on a community question-answering website.
87 Ranking for Relevance and Display Preferences in Complex Presentation Layouts Harrie Oosterhuis, Maarten de Rijke To address this gap we introduce a novel Deep Reinforcement Learning method that is capable of learning complex rankings, both the layout and the best ranking given the layout, from weak reward signals.
88 Natural Language Interfaces with Fine-Grained User Interaction: A Case Study on Web APIs Yu Su, Ahmed Hassan Awadallah, Miaosen Wang, Ryen W. White To facilitate this study, we propose a novel modular sequence-to-sequence model to create interactive natural language interfaces.
89 Pytrec_eval: An Extremely Fast Python Interface to trec_eval Christophe Van Gysel, Maarten de Rijke We introduce pytrec_eval, a Python interface to the trec_eval information retrieval evaluation toolkit.
90 Split-Lists and Initial Thresholds for WAND-based Search Andrew Kane, Frank Wm. Tompa Supported by extensive experiments, we suggest two approaches to improve query performance: initial list thresholds should be used when k values are large, and our split-list WAND approach should be used instead of the normal WAND or BMW approaches.
91 Ontology Evaluation with Path-based Text-aware Entropy Computation Ying Shen, Daoyuan Chen, Min Yang, Yaliang Li, Nan Du, Kai Lei To address these deficiencies, the present study proposes a Path-based Text-aware Entropy Computation method, PTEC, by considering the path information between different vertices and the textual information within the path to calculate the connectivity path of the whole network and the different weights between various nodes.
92 Reducing Variance in Gradient Bandit Algorithm using Antithetic Variates Method Sihao Yu, Jun Xu, Yanyan Lan, Jiafeng Guo, Xueqi Cheng To address the issue and inspired by the antithetic variates method for variance reduction, we propose to combine the antithetic variates method with traditional policy gradient for the multi-armed bandit problem.
93 A Flexible Forecasting Framework for Hierarchical Time Series with Seasonal Patterns: A Case Study of Web Traffic Zitao Liu, Yan Yan, Milos Hauskrecht In this work, we focus on models and analysis of multivariate time series data that are organized in hierarchies.
94 Query Performance Prediction using Passage Information Haggai Roitman We focus on the post-retrieval query performance prediction (QPP) task.
95 Users, Adaptivity, and Bad Abandonment Alistair Moffat, Alfan Farizki Wicaksono We consider two recent proposals for effectiveness metrics that have been argued to be adaptive, those of Moffat et al. (ACM TOIS, 2017) and Jiang and Allan (CIKM, 2017), and consider the user interaction models that they give rise to.
96 Knowledge-aware Attentive Neural Network for Ranking Question Answer Pairs Ying Shen, Yang Deng, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei In the paper, we propose KABLSTM, a Knowledge-aware Attentive Bidirectional Long Short-Term Memory, which leverages external knowledge from knowledge graphs (KG) to enrich the representational learning of QA sentences.
97 A Living Lab Study of Query Amendment in Job Search Bahar Salehi, Damiano Spina, Alistair Moffat, Sargol Sadeghi, Falk Scholer, Timothy Baldwin, Lawrence Cavedon, Mark Sanderson, Wilson Wong, Justin Zobel Of particular interest in this case study is a clear ‘success’ signal, namely, the number of job applications lodged by a user as a result of querying the service.
98 Attention-driven Factor Model for Explainable Personalized Recommendation Jingwu Chen, Fuzhen Zhuang, Xin Hong, Xiang Ao, Xing Xie, Qing He In this work, we propose the Attention-driven Factor Model (AFM), which can not only integrate item features driven by users’ attention but also help answer this "why".
99 IRevalOO: An Object Oriented Framework for Retrieval Evaluation Kevin Roitero, Eddy Maddalena, Yannick Ponte, Stefano Mizzaro We propose IRevalOO, a flexible Object Oriented framework that (i) can be used as-is as a replacement of the widely adopted trec\_eval software, and (ii) can be easily extended (or "instantiated”, in framework terminology) to implement different scenarios of test collection based retrieval evaluation.
100 Translating Representations of Knowledge Graphs with Neighbors Chun-Chih Wang, Pu-Jen Cheng In this paper, we propose a method named TransN, which consid- ers the dependencies between triples and incorporates neighbor information dynamically.
101 Index Compression for BitFunnel Query Processing Xinyu Liu, Zhaohua Zhang, Boran Hou, Rebecca J. Stones, Gang Wang, Xiaoguang Liu In order to reduce storage requirements, we propose a dictionary-based compression approach for the recently proposed bitwise data-structure BitFunnel, which makes use of a Bloom filter.
102 Large-Scale Image Retrieval with Elasticsearch Giuseppe Amato, Paolo Bolettieri, Fabio Carrara, Fabrizio Falchi, Claudio Gennaro In this paper, we propose to transform CNN features into textual representations and index them with the well-known full-text retrieval engine Elasticsearch.
103 A Co-Memory Network for Multimodal Sentiment Analysis Nan Xu, Wenji Mao, Guandan Chen To fill this gap, in this paper, we consider the interrelation of visual and textual information, and propose a novel co-memory network to iteratively model the interactions between visual contents and textual words for multimodal sentiment analysis.
104 Investigating User Perception of Gender Bias in Image Search: The Role of Sexism Jahna Otterbacher, Alessandro Checco, Gianluca Demartini, Paul Clough We conduct a controlled experiment via crowdsourcing using participants recruited from three countries to measure the extent to which workers perceive a given image results set to be subjective or objective.
105 Killing Two Birds With One Stone: Concurrent Ranking of Tags and Comments of Social Images Boon-Siew Seah, Aixin Sun, Sourav S Bhowmick In this paper, we present a lightweight visual signature-based model to concurrently generate ranked lists of comments and tags of a social image based on their joint relevance to the visual features, user comments, and user tags.
106 Imagination Based Sample Construction for Zero-Shot Learning Gang Yang, Jinlu Liu, Xirong Li Different from these existing types of methods, we propose a new method: sample construction to deal with the problem of ZSL.
107 Parameterizing Kterm Hashing Dominik Wurzer, Yumeng Qin In this paper, we focus on improving the effectiveness of Kterm Hashing.
108 Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to Reviewers Jie Zou, Dan Li, Evangelos Kanoulas The goal of a technology-assisted review is to achieve high recall with low human effort.
109 An Information Retrieval Framework for Contextual Suggestion Based on Heterogeneous Information Network Embeddings Dominic Seyler, Praveen Chandar, Matthew Davis We present an Information Retrieval framework that leverages Heterogeneous Information Network (HIN) embeddings for contextual suggestion.
110 Toward an Interactive Patent Retrieval Framework based on Distributed Representations Walid Shalaby, Wlodek Zadrozny We present a novel interactive framework for patent retrieval leveraging distributed representations of concepts and entities extracted from the patents text.
111 Consistency and Variation in Kernel Neural Ranking Model Mary Arpita Pyreddy, Varshini Ramaseshan, Narendra Nath Joshi, Zhuyun Dai, Chenyan Xiong, Jamie Callan, Zhiyuan Liu This paper studies the consistency of the kernel-based neural ranking model K-NRM, a recent state-of-the-art neural IR model, which is important for reproducible research and deployment in the industry.
112 Review Sentiment-Guided Scalable Deep Recommender System Dongmin Hyun, Chanyoung Park, Min-Chul Yang, Ilhyeon Song, Jung-Tae Lee, Hwanjo Yu In this work, we present a scalable review-aware recommendation method, called SentiRec, that is guided to incorporate the sentiments of reviews when modeling the users and the items.
113 Learning to Detect Pathogenic Microorganism of Community-acquired Pneumonia Wenwei Liang, Wei Zhang, Bo Jin, Jiangjiang Xu, Linhua Shu, Hongyuan Zha In this paper, we formulate a new problem for automatically detecting pathogenic microorganism of CAP by considering patient biomedical features from EHRs, including time-varying body temperatures and common laboratory measurements.
114 Towards Distributed Pairwise Ranking using Implicit Feedback Mohsan Jameel, Nicolas Schilling, Lars Schmidt-Thieme In this paper we address the scalability aspect of a pairwise ranking method using Factorization Machines in distributed settings.
115 Semantic Location in Email Query Suggestion John Foley, Mingyang Zhang, Michael Bendersky, Marc Najork We present a simple but effective model that can use location to predict queries for a user even before they type anything into a search box, and which learns effectively even when not all queries have location information.
116 GraphCAR: Content-aware Multimedia Recommendation with Graph Autoencoder Qidi Xu, Fumin Shen, Li Liu, Heng Tao Shen To fill this gap, we propose a Content-aware Multimedia Recommendation Model with Graph Autoencoder (GraphCAR), combining informative multimedia content with user-item interaction.
117 Multi-level Abstraction Convolutional Model with Weak Supervision for Information Retrieval Yifan Nie, Alessandro Sordoni, Jian-Yun Nie To cope with this problem, we propose a multi-level abstraction convolutional model (MACM) that generates and aggregates several levels of matching scores.
118 Analyzing and Characterizing User Intent in Information-seeking Conversations Chen Qu, Liu Yang, W. Bruce Croft, Johanne R. Trippas, Yongfeng Zhang, Minghui Qiu In this paper, we introduce a new dataset designed for this purpose and use it to analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns.
119 Modeling Multidimensional User Relevance in IR using Vector Spaces Sagar Uprety, Yi Su, Dawei Song, Jingfei Li We propose a geometric model inspired by the mathematical framework of Quantum theory to capture the user’s importance given to each dimension of relevance and test our hypothesis on data from a web search engine and TREC Session track
120 Are we on the Right Track?: An Examination of Information Retrieval Methodologies Enrique Amigó, Hui Fang, Stefano Mizzaro, ChengXiang Zhai With the hope of improving IR research practice, we propose a general methodology for IR that integrates the strengths of existing research methods.
121 Fine-Grained Information Identification in Health Related Posts Hamed Khanpour, Cornelia Caragea In this paper, we propose a computational model that mines user content in online health communities to detect positive experiences and suggestions on health improvement as well as negative impacts or side effects that cause suffering throughout fighting with a disease.
122 Quantitative Information Extraction From Social Data Omar Alonso, Thibault Sellam We propose the problem of identifying relevant quantitative information from social data as annotations for a topic.
123 Measuring Influence on Instagram: A Network-Oblivious Approach Noam Segev, Noam Avigdor, Eytan Avigdor In our work, we present and discuss the lack of relevant tools and insufficient metrics for influence measurement, focusing on a network oblivious approach and show that the graph-based approach used in other OSNs is a poor fit for Instagram.
124 Event2Vec: Neural Embeddings for News Events Vinay Setty, Katja Hose In this paper we propose Event2Vec, a novel way to learn latent feature vectors for news events using a network.
125 Universal Approximation Functions for Fast Learning to Rank: Replacing Expensive Regression Forests with Simple Feed-Forward Networks Daniel Cohen, John Foley, Hamed Zamani, James Allan, W. Bruce Croft We propose an alternate method for optimizing the execution of learned models: converting these expensive ensembles to a feed-forward neural network.
126 Modeling Mobile User Actions for Purchase Recommendation using Deep Memory Networks Djordje Gligorijevic, Jelena Gligorijevic, Aravindan Raghuveer, Mihajlo Grbovic, Zoran Obradovic This study proposes a novel approach to learn representations of mobile user actions using Deep Memory Networks.
127 Cross Domain Regularization for Neural Ranking Models using Adversarial Learning Daniel Cohen, Bhaskar Mitra, Katja Hofmann, W. Bruce Croft We use an adversarial discriminator and train our neural ranking model on a small set of domains.
128 Affective Representations for Sarcasm Detection Ameeta Agrawal, Aijun An In this paper, we introduce a novel model for automated sarcasm detection in text, called Affective Word Embeddings for Sarcasm (AWES), which incorporates affective information into word representations.
129 A User Study on Snippet Generation: Text Reuse vs. Paraphrases Wei-Fan Chen, Matthias Hagen, Benno Stein, Martin Potthast The paper in hand gives a first answer.
130 New Embedded Representations and Evaluation Protocols for Inferring Transitive Relations Sandeep Subramanian, Soumen Chakrabarti In this short research note, we make three contributions specific to representing and inferring transitive relations.
131 Taxi or Hitchhiking: Predicting Passenger’s Preferred Service on Ride Sharing Platforms Lingyu Zhang, Wei Ai, Chuan Yuan, Yuhui Zhang, Jieping Ye In this paper, we empirically analyze the preferred services and propose a recommender system which provides service recommendation based on temporal, spatial, and behavioral features.
132 Towards Intent-Aware Contextual Music Recommendation: Initial Experiments Sergey Volokhin, Eugene Agichtein In this paper, we perform initial exploration of the dominant music listening intents associated with common activities, using music retrieved from popular online music services.
133 Deep Domain Adaptation Based on Multi-layer Joint Kernelized Distance Sitong Mao, Xiao Shen, Fu-lai Chung In this paper, by utilizing deep features extracted from the deep networks, we proposed to compute the multi-layer joint kernelized mean distance between the k th target data predicted as the i th category and all the source data of the j th category $d_ij ^k$.
134 Do Not Pull My Data for Resale: Protecting Data Providers Using Data Retrieval Pattern Analysis Guosai Wang, Shiyang Xiang, Yitao Duan, Ling Huang, Wei Xu In this work, we define the "anti-data-reselling" problem and propose a new systematic method that combines feature engineering and machine learning models to provide a solution.
135 K-plet Recurrent Neural Networks for Sequential Recommendation Xiang Lin, Shuzi Niu, Yiqiao Wang, Yucheng Li In recommendation system, an optimal model should not only capture the global structure, but also the localized relationships.
136 Citation Worthiness of Sentences in Scientific Reports Hamed Bonab, Hamed Zamani, Erik Learned-Miller, James Allan In this paper, we introduce the task of citation worthiness for scientific texts at a sentence-level granularity. We construct a dataset using the ACL Anthology Reference Corpus; consisting of over 1.1M "not_cite" and 85K "cite" sentences.
137 Ad Click Prediction in Sequence with Long Short-Term Memory Networks: an Externality-aware Model Weiwei Deng, Xiaoliang Ling, Yang Qi, Tunzi Tan, Eren Manavoglu, Qi Zhang In this paper, we propose a new approach to predict ad CTR in sequence which considers user browsing behavior and the impact of top ads quality to the current one.
138 Assessing the Readability of Web Search Results for Searchers with Dyslexia Adam Fourney, Meredith Ringel Morris, Abdullah Ali, Laura Vonessen In this paper we study the lexical and aesthetic features of web documents that may underlie this relationship.
139 Comparing Two Binned Probability Distributions for Information Access Evaluation Tetsuya Sakai Through experiments using artificial distributions as well as real ones from a dialogue evaluation task, we demonstrate that two cross-bin measures, namely, the Normalised Match Distance (NMD; a special case of the Earth Mover’s Distance) and the Root Symmetric Normalised Order-aware Divergence (RSNOD), are indeed substantially different from the bin-by-bin measures.Furthermore, RSNOD lies between the popular bin-by-bin measures and NMD in terms of how it behaves.
140 Robust Asymmetric Recommendation via Min-Max Optimization Peng Yang, Peilin Zhao, Vincent W. Zheng, Lizhong Ding, Xin Gao To address these issues, we propose a robust asymmetric recommendation model.
141 From the PRP to the Low Prior Discovery Recall Principle for Recommender Systems Rocío Cañamares, Pablo Castells We revisit the Probability Ranking Principle in the context of recommender systems.
142 Deep Learning for Epidemiological Predictions Yuexin Wu, Yiming Yang, Hiroshi Nishiura, Masaya Saitoh In this paper, we develop a deep learning framework, for the first time, to predict epidemiology profiles in the time-series perspective.
143 Query Variation Performance Prediction for Systematic Reviews Harrisen Scells, Leif Azzopardi, Guido Zuccon, Bevan Koopman When conducting systematic reviews, medical researchers heavily deliberate over the final query to pose to the information retrieval system.
144 Attention-based Hierarchical Neural Query Suggestion Wanyu Chen, Fei Cai, Honghui Chen, Maarten de Rijke We propose an AHNQS model that combines a hierarchical structure with a session-level neural network and a user-level neural network to model the short- and long-term search history of a user.
145 Texygen: A Benchmarking Platform for Text Generation Models Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, Yong Yu We introduce Texygen, a benchmarking platform to support research on open-domain text generation models.
146 CA-LSTM: Search Task Identification with Context Attention based LSTM Cong Du, Peng Shu, Yong Li In this paper, we present the first search session segmentation model that uses a long short-term memory (LSTM) network with an attention mechanism.
147 On the Volatility of Commercial Search Engines and its Impact on Information Retrieval Research – Jimmy, Guido Zuccon, Gianluca Demartini We studied the volatility of commercial search engines and reflected on its impact on research that uses them as basis of algorithmical techniques or for user studies.
148 Deep Semantic Text Hashing with Weak Supervision Suthee Chaidaroon, Travis Ebesu, Yi Fang Motivated by the recent success in machine learning that makes use of weak supervision, we employ unsupervised ranking methods such as BM25 to extract weak signals from training data.
149 Who is the Mr. Right for Your Brand?: — Discovering Brand Key Assets via Multi-modal Asset-aware Projection Yang Liu, Tobey H. Ko, Zhonglei Gu Owing to the growing strategic importance of these brand key assets, this paper presents a novel feature extraction method named Multi-modal Asset-aware Projection (M2A2P) to learn a discriminative subspace from the high-dimensional multi-modal social media data for effective brand key asset discovery.
150 Sogou-QCL: A New Dataset with Click Relevance Label Yukun Zheng, Zhen Fan, Yiqun Liu, Cheng Luo, Min Zhang, Shaoping Ma In this paper, we present a new dataset, Sogou-QCL, which contains 537,366 queries and five kinds of weak relevance labels for over 12 million query-document pairs.
151 Towards Designing Better Session Search Evaluation Metrics Mengyang Liu, Yiqun Liu, Jiaxin Mao, Cheng Luo, Shaoping Ma In this paper, we analyze how users’ perceptions of satisfaction accord with a series of session-level evaluation metrics.
152 Predicting Contradiction Intensity: Low, Strong or Very Strong? Ismail Badache, Sébastien Fournier, Adrian-Gabriel Chifu This paper investigates contradiction intensity in reviews exploiting different features such as variation of ratings and variation of polarities around specific entities (e.g. aspects, topics).
153 A Large-Scale Study of Mobile Search Examination Behavior Xiaochuan Wang, Ning Su, Zexue He, Yiqun Liu, Shaoping Ma In this work, based on the large-scale real search log collected from a popular commercial mobile search engine, we present a comprehensive analysis of examination behavior.
154 Ranking Without Learning: Towards Historical Relevance-based Ranking of Social Images Min Min Chew, Sourav S. Bhowmick, Adam Jatowt To this end, we propose a learning-agnostic technique that leverages Wikipedia to quantify historical relevance of images.
155 Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, Kun Gai In this paper, we model CVR in a brand-new perspective by making good use of sequential pattern of user actions, i.e., impression -> click -> conversion.
156 How Much is Too Much?: Whole Session vs. First Query Behaviors in Task Type Prediction Matthew Mitsui, Jiqun Liu, Chirag Shah We examine the opposite end: the first query.
157 Effectiveness Evaluation with a Subset of Topics: A Practical Approach Kevin Roitero, Michael Soprano, Stefano Mizzaro We propose such a practical criterion for topic selection: we rely on the methods for automatic system evaluation without relevance judgments, and by running some experiments on several TREC collections we show that the topics selected on the basis of those evaluations are indeed more informative than random topics.
158 Locality-adapted Kernel Densities for Tweet Localization Ozer Ozdikis, Heri Ramampiaro, Kjetil Nørvåg We propose a location prediction method for tweets based on the geographical probability distribution of their terms over a region.
159 Related or Duplicate: Distinguishing Similar CQA Questions via Convolutional Neural Networks Wei Emma Zhang, Quan Z. Sheng, Zhejun Tang, Wenjie Ruan To tackle this issue, we propose to leverage neural network architecture to extract "deep" features to identify whether a question pair is duplicate or related.
160 Procrastination is the Thief of Time: Evaluating the Effectiveness of Proactive Search Systems Procheta Sen, Debasis Ganguly, Gareth Jones We propose a framework to evaluate such a search system.
161 Convolution-based Memory Network for Aspect-based Sentiment Analysis Chuang Fan, Qinghong Gao, Jiachen Du, Lin Gui, Ruifeng Xu, Kam-Fai Wong Targeting this problem, we propose a novel convolutional memory network which incorporates an attention mechanism.
162 WikiPassageQA: A Benchmark Collection for Research on Non-factoid Answer Passage Retrieval Daniel Cohen, Liu Yang, W. Bruce Croft In this paper, we introduce a new Wikipedia based collection specific for non-factoid answer passage retrieval containing thousands of questions with annotated answers and show benchmark results on a variety of state of the art neural architectures and retrieval models.
163 Beyond Pooling Gordon V. Cormack, Maura R. Grossman Beyond Pooling
164 Testing the Cluster Hypothesis with Focused and Graded Relevance Judgments Eilon Sheetrit, Anna Shtok, Oren Kurland, Igal Shprincis We present novel tests that utilize graded and focused relevance judgments; the latter are markups of relevant text in relevant documents.
165 Query Performance Prediction Focused on Summarized Letor Features Adrian-Gabriel Chifu, Léa Laporte, Josiane Mothe, Md Zia Ullah In this paper we investigate the use of features that were initially defined for learning to rank in the task of QPP.
166 A Study of Per-Topic Variance on System Comparison Meng Yang, Peng Zhang, Dawei Song In this paper, we study the effect of per-topic variance on the system comparison.
167 Online Job Search: Study of Users’ Search Behavior using Search Engine Query Logs Behrooz Mansouri, Mohammad Sadegh Zahedi, Ricardo Campos, Mojgan Farhoodi In this paper, we explore the different characteristics of online job search and their differences with general searches, by leveraging search engine query logs.
168 Identifying and Modeling Information Resumption Behaviors in Cross-Device Search Dan Wu, Jing Dong, Yuan Tang Enlightened by task resumption behaviors in cross-session search, we explore information resumption behaviors in cross-device search.
169 Optimizing Query Evaluations Using Reinforcement Learning for Web Search Corby Rosset, Damien Jose, Gargi Ghosh, Bhaskar Mitra, Saurabh Tiwary In this work, we pose match planning as a reinforcement learning task and observe up to 20% reduction in index blocks accessed, with small or no degradation in the quality of the candidate sets.
170 SAAN: A Sentiment-Aware Attention Network for Sentiment Analysis Zeyang Lei, Yujiu Yang, Min Yang In this paper, we propose a Sentiment-Aware Attention Network (SAAN) to boost the performance of sentiment analysis, which adopts a three-step strategy to learn the sentiment-specific sentence representation.
171 An Attribute-aware Neural Attentive Model for Next Basket Recommendation Ting Bai, Jian-Yun Nie, Wayne Xin Zhao, Yutao Zhu, Pan Du, Ji-Rong Wen In this paper, we propose a novel Attribute-aware Neural Attentive Model (ANAM) to address these problems.
172 Characterizing Question Facets for Complex Answer Retrieval Sean MacAvaney, Andrew Yates, Arman Cohan, Luca Soldaini, Kai Hui, Nazli Goharian, Ophir Frieder In this work, we present two novel approaches for CAR based on the observation that question facets can vary in utility: from structural (facets that can apply to many similar topics, such as ‘History’) to topical (facets that are specific to the question’s topic, such as the ‘Westward expansion’ of the United States).
173 A Test Collection for Coreferent Mention Retrieval Rashmi Sankepally, Tongfei Chen, Benjamin Van Durme, Douglas W. Oard This paper introduces the coreferent mention retrieval task, in which the goal is to retrieve sentences that mention a specific entity based on a query by example in which one sentence mentioning that entity is provided.
174 What Do Viewers Say to Their TVs?: An Analysis of Voice Queries to Entertainment Systems Jinfeng Rao, Ferhan Ture, Jimmy Lin We present an analysis of a large query log from this service to answer the question: "What do viewers say to their TVs?"
175 Sanity Check: A Strong Alignment and Information Retrieval Baseline for Question Answering Vikas Yadav, Rebecca Sharp, Mihai Surdeanu Here we propose an unsupervised, simple, and fast alignment and informa- tion retrieval baseline that incorporates two novel contributions: a one-to-many alignment between query and document terms and negative alignment as a proxy for discriminative information.
176 Explaining Controversy on Social Media via Stance Summarization Myungha Jang, James Allan In this paper, we study methods to generate a stance-aware summary that explains a given controversy by collecting arguments of two conflicting stances.
177 Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks Vaibhav Kumar, Dhruv Khattar, Siddhartha Gairola, Yash Kumar Lal, Vasudeva Varma We propose a novel approach considering all information found in a social media post.
178 Multi-Target Stance Detection via a Dynamic Memory-Augmented Network Penghui Wei, Junjie Lin, Wenji Mao In this paper, we propose a dynamic memory-augmented network DMAN for multi-target stance detection.
179 Query Performance Prediction and Effectiveness Evaluation Without Relevance Judgments: Two Sides of the Same Coin Stefano Mizzaro, Josiane Mothe, Kevin Roitero, Md Zia Ullah We propose to use those methods, and their combination based on a machine learning approach, for query performance prediction.
180 A New Term Frequency Normalization Model for Probabilistic Information Retrieval Fanghong Jian, Jimmy Xiangji Huang, Jiashu Zhao, Tingting He In this paper, we assume and show empirically that term frequency normalization should be specific with query length in order to optimize retrieval performance.
181 Transparent Tree Ensembles Alexander Moore, Vanessa Murdock, Yaxiong Cai, Kristine Jones In this work we present a method for deriving explanations for instance-level decisions in tree ensembles.
182 A Taxonomy of Queries for E-commerce Search Parikshit Sondhi, Mohit Sharma, Pranam Kolari, ChengXiang Zhai In this paper, we share the first empirical study of the queries and search behavior of users in E-Com search by analyzing search log from a major E-Com search engine.
183 Theoretical Analysis of Interdependent Constraints in Pseudo-Relevance Feedback Ali Montazeralghaem, Hamed Zamani, Azadeh Shakery In this paper, we revisit this assumption and hypothesize that there might be interdependence relationships between the existing constraints.
184 Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only Robert Litschko, Goran Glavaš, Simone Paolo Ponzetto, Ivan Vulić We propose a fully unsupervised framework for ad-hoc cross-lingual information retrieval (CLIR) which requires no bilingual data at all.
185 Toward Voice Query Clarification Johannes Kiesel, Arefeh Bahrami, Benno Stein, Avishek Anand, Matthias Hagen We conduct a user study that measures the satisfaction for clarifications that are explicitly invoked and presented by seven different methods.
186 A Test Collection for Evaluating Legal Case Law Search Daniel Locke, Guido Zuccon In this paper, we present a test collection for use in evaluating case law search, being the retrieval of judicial decisions relevant to a particular legal question.
187 SearchX: Empowering Collaborative Search Research Sindunuraga Rikarno Putra, Felipe Moraes, Claudia Hauff In this paper, we present SearchX, an open-source collaborative search system we are currently developing-and using for our research.
188 A/B Testing with APONE Mónica Marrero, Claudia Hauff Academic researchers usually develop adhoc solutions, leading to many duplicated efforts and time spent on work not directly related to one’s research.
189 CoNEREL: Collective Information Extraction in News Articles Minh C. Phan, Aixin Sun We present CoNEREL, a system for collective named entity recognition and entity linking focusing on news articles and readers’ comments.
190 A2A: Benchmark Your Clinical Decision Support Search Sarvnaz Karimi, Vincent Nguyen, Falk Scholer, Brian Jin, Sara Falamaki We developed a platform to facilitate experimentation and hypothesis testing for information retrieval researchers working on this topic.
191 An Information Retrieval Experiment Framework for Domain Specific Applications Harrisen Scells, Daniel Locke, Guido Zuccon We present a framework for constructing and executing information retrieval experiment pipelines.
192 Vote Goat: Conversational Movie Recommendation Jeffrey Dalton, Victor Ajayi, Richard Main In this demonstration we introduce Vote Goat, a conversational recommendation agent built using Google’s DialogFlow framework.
193 LogCanvas: Visualizing Search History Using Knowledge Graphs Luyan Xu, Zeon Trevor Fernando, Xuan Zhou, Wolfgang Nejdl In this demo paper, we introduce LogCanvas, a platform for user search history visualization.Different from the existing visualization tools, LogCanvas focuses on helping users re-construct the semantic relationship among their search activities.
194 API Caveat Explorer — Surfacing Negative Usages from Practice: An API-oriented Interactive Exploratory Search System for Programmers Jing Li, Aixin Sun, Zhenchang Xing, Lei Han In this demonstration, we present API Caveat Explorer, a search system to explore API caveats that are mined from large-scale unstructured discussions on Stack Overflow.
195 SmartTable: A Spreadsheet Program with Intelligent Assistance Shuo Zhang, Vugar Abdul Zada, Krisztian Balog We introduce SmartTable, an online spreadsheet application that is equipped with intelligent assistance capabilities.
196 Interactive Symptom Elicitation for Diagnostic Information Retrieval Tuukka Ruotsalo, Antti Lipsanen We demonstrate interactive symptom elicitation for diagnostic information retrieval.
197 Sover! Social Media Observer Asmelash Teka Hadgu, Sallam Abualhaija, Claudia Niederée For this purpose we have developed and demonstrate Sover!
198 Combining Terrier with Apache Spark to create Agile Experimental Information Retrieval Pipelines Craig Macdonald We argue that this (1) provides an agile experimental platform for information retrieval, comparable to that enjoyed by other branches of data science; (2) aids research reproducibility in information retrieval by facilitating easily-distributable notebooks containing conducted experiments; and (3) facilitates the teaching of information retrieval experiments in educational environments.
199 Dynamic Composition of Question Answering Pipelines with FRANKENSTEIN Kuldeep Singh, Ioanna Lytra, Arun Sethupat Radhakrishna, Akhilesh Vyas, Maria-Esther Vidal In this paper, we illustrate different functionalities of FRANKENSTEIN for performing independent QA component execution, QA component prediction, given an input question as well as the static and dynamic composition of different QA pipelines.
200 A System for Efficient High-Recall Retrieval Mustafa Abualsaud, Nimesh Ghelani, Haotian Zhang, Mark D. Smucker, Gordon V. Cormack, Maura R. Grossman In this paper, we present the design of our system that affords efficient high-recall retrieval.
201 HyPlag: A Hybrid Approach to Academic Plagiarism Detection Norman Meuschke, Vincent Stange, Moritz Schubotz, Bela Gipp To improve upon the detection capabilities for such concealed content reuse in academic publications, we make four contributions: i) We present the first plagiarism detection approach that combines the analysis of mathematical expressions, images, citations and text.
202 RecAdvisor: Criteria-based Ph.D. Supervisor Recommendation Mir Anamul Hasan, Daniel G. Schwartz RecAdvisor: Criteria-based Ph.D. Supervisor Recommendation
203 Minority Report by Lemur: Supporting Search Engine with Virtual Reality Andrew Jie Zhou, Grace Hui Yang In this paper, we introduce a Virtual Reality (VR) search engine interface.
204 Hide-n-Seek: An Intent-aware Privacy Protection Plugin for Personalized Web Search Puxuan Yu, Wasi Uddin Ahmad, Hongning Wang With a variety of graphical user interfaces, we present the topic-based query obfuscation mechanism to the end users for them to digest how their search privacy is protected.
205 Machine Learning @ Amazon Rajeev Rastogi I will then talk about two specific applications where we use a variety of methods to learn semantically rich representations of data: question answering where we use deep learning techniques and product size recommendations where we use probabilistic models.
206 Extracting Real-Time Insights from Graphs and Social Streams Charu C. Aggarwal We discuss recent advances in several graph mining applications like clustering, classification, link prediction, event detection, and anomaly detection in real-time graph streams.
207 Big Data at Didi Chuxing Jieping Ye In this talk, I will explain how Didi Chuxing applies big data and AI technologies to analyze big transportation data and improve the travel experience for millions of users.
208 The City Brain: Towards Real-Time Search for the Real-World Xian-Sheng Hua The City Brain: Towards Real-Time Search for the Real-World
209 Causal Inference over Longitudinal Data to Support Expectation Exploration Emre Kıcıman In this presentation, we show how causal inference methods can be applied to such individual-level, longitudinal records to generate answers for expectation exploration queries.
210 Merchandise Recommendation for Retail Events with Word Embedding Weighted Tf-idf and Dynamic Query Expansion Ted Tao Yuan, Zezhong Zhang With feedback to expand query scope, we discuss keyword expansion candidate selection using word embedding similarity, and an enhanced tf-idf formula for expanded words in search ranking.
211 Product Question Answering Using Customer Generated Content – Research Challenges David Carmel, Liane Lewin-Eytan, Yoelle Maarek Product Question Answering Using Customer Generated Content – Research Challenges
212 Auto-completion for Question Answering Systems at Bloomberg Konstantine Arkoudas, Mohamed Yahya At that point we can execute the queries against a suitable back end, obtain the results, and present them to the users.
213 Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned Sahin Cem Geyik, Qi Guo, Bo Hu, Cagri Ozcaglar, Ketan Thakkar, Xianren Wu, Krishnaram Kenthapadi In this talk, we present the overall system design and architecture, the challenges encountered in practice, and the lessons learned from the production deployment of the talent search and recommendation systems at LinkedIn.
214 The Evolution of Content Analysis for Personalized Recommendations at Twitter Ajeet Grewal, Jimmy Lin We present a broad overview of personalized content recommendations at Twitter, discussing how our approach has evolved over the years, represented by several generations of systems.
215 Large Scale Search Engine Marketing (SEM) at Airbnb James Wong, Brendan Collins, Ganesh Venkataraman In this talk, we will describe how ad- vertising efficiently on these platforms requires solving several information retrieval and machine learning problems, including query understanding, click value estimation, and realtime pacing of our expenditure.
216 Clova: Services and Devices Powered by AI Inho Kang In this talk, I will cover some efforts and challenges in understanding and satisfying users on each device with sophisticated natural language processing technologies. I will introduce the technically challenging problems that we are currently tackling and future AI developments.
217 Lessons from Building a Large-scale Commercial IR-based Chatbot for an Emerging Market Manoj Kumar Chinnakotla, Puneet Agrawal In this work, we highlight some interesting challenges faced when trying to build a large-scale commercial IR-based chatbot, Ruuh, for an emerging market like India which has unique characteristics such as high linguistic and cultural diversity, large section of young population and the second largest mobile market in the world.
218 LessonWare: Mining Student Notes to Provide Personalized Feedback Perry Samson, Charles Bassam A new educational service has been prototyped by Echo360 that uses natural language processing to analyze students notes and provide personalized recommendations on how to both improve note-taking and scaffold learning.
219 Deep Learning for Matching in Search and Recommendation Jun Xu, Xiangnan He, Hang Li In this tutorial, we aim to give a comprehensive survey on recent progress in deep learning for matching in search and recommendation. Firstly, we introduce the general problem of matching, which is fundamental in both search and recommendation.
220 Conducting Laboratory Experiments Properly with Statistical Tools: An Easy Hands-on Tutorial Tetsuya Sakai Part I covers the following topics: paired and two-sample t -tests, confidence intervals (with Excel and R); familywise error rate, multiple comparison procedures; ANOVA (with Excel and R); Tukey’s HSD test, simultaneous confidence intervals (with R).
221 Neural Approaches to Conversational AI Jianfeng Gao, Michel Galley, Lihong Li For each category, we present a review of state-of-the-art neural approaches, draw the connection between neural approaches and traditional symbolic approaches, and discuss the progress we have made and challenges we are facing, using specific systems and models as case studies.
222 Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances Weinan Zhang In this tutorial, we focus on discussing the GAN techniques and the variants on discrete data fitting in various information retrieval scenarios.
223 Information Discovery in E-commerce: Half-day SIGIR 2018 Tutorial Zhaochun Ren, Xiangnan He, Dawei Yin, Maarten de Rijke Below we describe why we believe that the time is right for an introductory tutorial on information discovery in e-commerce, the objectives of the proposed tutorial, its relevance, as well as more practical details, such as the format, schedule and support materials.
224 Fusion in Information Retrieval: SIGIR 2018 Half-Day Tutorial Oren Kurland, J. Shane Culpepper The goal of this half day, intermediate-level, tutorial is to provide a methodological view of the theoretical foundations of fusion approaches, the numerous fusion methods that have been devised and a variety of applications for which fusion techniques have been applied.
225 Utilizing Knowledge Graphs for Text-Centric Information Retrieval Laura Dietz, Alexander Kotov, Edgar Meij Utilizing Knowledge Graphs for Text-Centric Information Retrieval
226 SIGIR 2018 Tutorial on Health Search (HS2018): A Full-day from Consumers to Clinicians Guido Zuccon, Bevan Koopman The HS2018 tutorial will cover topics from an area of information retrieval (IR) with significant societal impact — health search.
227 A Tutorial on Probabilistic Topic Models for Text Data Retrieval and Analysis ChengXiang Zhai, Chase Geigle The tutorial provides (1) an in-depth explanation of the basic concepts, underlying principles, and the two basic topic models (i.e., PLSA and LDA) that have widespread applications, (2) an introduction to EM algorithms and Bayesian inference algorithms for topic models, (3) a hands-on exercise to allow the tutorial attendants to learn how to use the topic models implemented in the MeTA Open Source Toolkit and experiment with provided data sets, (4) a broad overview of all the major representative topic models that extend PLSA or LDA, and (5) a discussion of major challenges and future research directions.
228 Knowledge Extraction and Inference from Text: Shallow, Deep, and Everything in Between Soumen Chakrabarti We will present a comprehensive catalog of the best practices in traditional and deep knowledge extraction, inference and search.
229 Efficient Query Processing Infrastructures: A half-day tutorial at SIGIR 2018 Nicola Tonellotto, Craig Macdonald Efficient Query Processing Infrastructures: A half-day tutorial at SIGIR 2018
230 SIGIR 2018 Workshop on eCommerce (ECOM18) Jon Degenhardt, Pino Di Fabbrizio, Surya Kallumadi, Mohit Kumar, Yiu-Chang Lin, Andrew Trotman, Huasha Zhao The purpose of this workshop is to bring together researchers and practitioners of eCommerce IR to discuss topics unique to it, to set a research agenda, and to examine how to build datasets for research into this fascinating topic.
231 SIGIR 2018 Workshop on ExplainAble Recommendation and Search (EARS 2018) Yongfeng Zhang, Yi Zhang, Min Zhang Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also intuitive explanations of the results for users or system designers, which can help to improve the system transparency, persuasiveness, trustworthiness, and effectiveness, etc.
232 Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2018) Muthu Kumar Chandrasekaran, Kokil Jaidka, Philipp Mayr To this purpose, we propose the third iteration of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL).
233 DATA: SEARCH’18 – Searching Data on the Web Paul Groth, Laura Koesten, Philipp Mayr, Maarten de Rijke, Elena Simperl We welcome contributions describing algorithms and systems, as well as frameworks and studies in human data interaction.
234 The Second Workshop on Knowledge Graphs and Semantics for Text Retrieval, Analysis, and Understanding (KG4IR) Laura Dietz, Chenyan Xiong, Jeff Dalton, Edgar Meij The goal of this workshop is to bring together and grow a community of researchers and practitioners who are interested in using, aligning, and constructing knowledge graphs and similar semantic resources for information retrieval applications.
235 Computational Surprise in Information Retrieval Xi Niu, Wlodek Zadrozny, Kazjon Grace, Weimao Ke The themes in this workshop include discussion of what can be learned from some well-known surprise models in other fields, such as Bayesian surprise; how to evaluate surprise based on user experience; and how computational surprise is related to the newly emerging areas, such as fake news detection, computational contradiction, clickbait detection, etc.
236 First International Workshop on Professional Search (ProfS2018) Suzan Verberne, Jiyin He, Udo Kruschwitz, Birger Larsen, Tony Russell-Rose, Arjen P. de Vries The aim of this workshop is to bring together researchers to work on the requirements and challenges of professional search from different angles.
237 Second International Workshop on Conversational Approaches to Information Retrieval (CAIR’18): Workshop at SIGIR 2018 Jaime Arguello, Filip Radlinski, Hideo Joho, Damiano Spina, Julia Kiseleva A specific focus will be on techniques that support complex and multi-turn user-machine dialogues for information access and retrieval, and multi-modal interfaces for interacting with such systems.
238 SIGIR 2018 Workshop on Learning from Limited or Noisy Data for Information Retrieval Hamed Zamani, Mostafa Dehghani, Fernando Diaz, Hang Li, Nick Craswell The goal of this workshop is to bring together researchers from industry—where data is plentiful but noisy—with researchers from academia—where data is sparse but clean to discuss solutions to these related problems.
239 SIGIR 2018 Workshop on Intelligent Transportation Informatics Yan Liu, Zhenhui Li, Wei Ai, Lingyu Zhang We propose a half-day workshop at SIGIR 2018 for the professionals, researchers, and practitioners who are interested in mining and understanding big and heterogeneous data generated in transportation to improve the transportation system.
240 Exploring Potential Pathways to Address Bias and Ethics in IR Steven Zimmerman Using existing knowledge about human bias and profile data, we propose leveraging this information to raise awareness to users about their behavior in the frame of IR systems and inferences made.
241 SmartTable: Equipping Spreadsheets with Intelligent AssistanceFunctionalities Shuo Zhang The objective of this research is to develop a set of components for a tool called SmartTable, which is aimed at assisting the user in completing a complex task by providing intelligent assistance for working with tables. Motivated by the above scenario, we propose a set of novel tasks such as row and column heading population, table search, and table generation.
242 Efficiency-Effectiveness Trade-Offs in Machine Learned Models for Information Retrieval Luke Gallagher Efficiency-Effectiveness Trade-Offs in Machine Learned Models for Information Retrieval
243 Case-Based Retrieval Using Document-Level Semantic Networks Stefano Marchesin We propose a research that aims at improving the effectiveness of case-based retrieval systems through the use of automatically created document-level semantic networks.
244 Utilizing Inter-Passage Similarities for Focused Retrieval Eilon Sheetrit Our main goal is studying the merits of using inter-passage similarities for the task of focused retrieval; i.e., ranking passages in documents by their relevance to an information need expressed by a query.
245 Enhanced Contextual Recommendation using Social Media Data Anirban Chakraborty I propose a log-linear retrieval model for document ranking using Kernel Density Estimation (KDE) which will eventually rank POIs.
246 A Semantic Search Approach to Task-Completion Engines Darío Garigliotti One component we focus on in this research is utilizing entity type information, to gain a better understanding of how entity type information can be exploited in entity retrieval.
247 Addressing News-Related Standing Information Needs Kristine M. Rogers A user with a standing need for updates on current events uses a structured exploration process for finding and reviewing new documents, with the user comparing document information to her mental model.
248 Improving Systematic Review Creation With Information Retrieval Harrisen Scells The objective of this research is to use Information Retrieval techniques to improve the retrieval of literature for medical systematic reviews.
249 Design and Evaluation of Query Auto Completion Mechanisms Unni Krishnan Design and Evaluation of Query Auto Completion Mechanisms
250 Better Textbooks with Human Language Technology Sudeshna Das In this thesis we focus on the following research questions.
251 CHEERS: CHeap & Engineered Evaluation of Retrieval Systems Kevin Roitero In this paper we present our proposal for a more engineered approach to information retrieval evaluation.