Keynote Speakers

Dr. Jianfeng Gao, Microsoft Research AI

TITLE: Towards an open-domain dialog system


Developing an intelligent dialogue system that not only emulates human conversation, but also can answer questions of topics ranging from latest news of a movie star to Einstein’s theory of relativity, and fulfill complex tasks such as travel planning, has been one of the longest running goals in AI. In this talk, we use Microsoft XiaoIce as a case study to discuss the state-of-the-art conversational AI, focusing on three types of dialogues: (1) question answering bots that can provide concise direct answers to user queries; (2) task-oriented bots that can help users accomplish tasks ranging from meeting scheduling to vacation planning; and (3) social bots which can converse seamlessly and appropriately with humans, and often plays roles of a chat companion and a recommender.


Dr. Jianfeng Gao is Partner Research Manager at Microsoft Research AI. He leads the development of AI systems for machine reading comprehension (MRC), question answering (QA), social bots, goal-oriented dialogue, and business applications. From 2014 to 2017, he was Partner Research Manager at Deep Learning Technology Center at Microsoft Research, Redmond, where he was leading the research on deep learning for text and image processing. From 2006 to 2014, he was Principal Researcher at Natural Language Processing Group at Microsoft Research, Redmond, where he worked on Web search, query understanding and reformulation, ads prediction, and statistical machine translation. From 2005 to 2006, he was a Research Lead in Natural Interactive Services Division at Microsoft, where he worked on Project X, an effort of developing natural user interface for Windows. From 2000 to 2005, he was Research Lead in Natural Language Computing Group at Microsoft Research Asia, where he and his colleagues developed the first Chinese speech recognition system released with Microsoft Office, the Chinese/Japanese Input Method Editors (IME) which were the leading products in the market, and the natural language platform for Microsoft Windows.

Dr. Diane Kelly, University of Tennessee

TITLE: What Do We Mean by Theory in Information Retrieval?


Theory comes in many forms. In some fields, mathematical statements are used to communicate theory, while in others, verbal statements are used. Some fields rely heavily on models, such as physical, mechanical or stochastic, while other fields rely on simulation. As an area of study, information retrieval (IR) addresses topics and problems that can be informed by a wide-variety of theories and models, including those used to describe the actions of machines, as well as those used to explain the behaviors of humans and systems. While theoretical research is often presented as being at odds with empirical research, in reality, they cannot be separated.
In this talk, I will review several theories and models that have guided select IR research, and discuss the ways researchers have exercised, explored and tested theories. I will also discuss the role of theories in IR research, and present criteria that can be used to reason about whether an IR theory is useful. I will argue that we should demand more theory from IR research. In particular, while the absence of theory does not prevent us from doing research, it does restrict our findings to a narrow slice of time.


Diane Kelly is Professor and Director at the School of Information Sciences at the University of Tennessee. Prior to this, she was a professor at the University of North Carolina at Chapel Hill. Her research and teaching interests are in interactive information search and retrieval, information search behavior, and research methods. She has received several awards for both her research and teaching, including the ASIST Research Award, British Computer Society’s IRSG Karen Spärck Jones Award, and the ASIST/Thomson Reuters Outstanding Information Science Teacher Award. Kelly is the past chair of ACM SIGIR, associate editor of ACM Transactions on Information Systems and serves on the editorial boards of several journals including, Foundations and Trends in Information Retrieval, Information Processing & Management, and Information Retrieval Journal.


Jan Pedersen

Distinguished Scientist, Uber AI

Marc Najork

Research Engineering Director, Google AI

Eugene Agichtein

Professor at Emory University and “Amazon Scholar” at Amazon Research

Accepted Long Papers

Title Authors
Utilizing Passages in Fusion-based Document Retrieval Haggai Roitman and Yosi Mass
Neural Attentive Cross-Domain Recommendation Dimitrios Rafailidis and Fabio Crestani
How Fair Can We Go: Detecting the Boundaries of Fairness Optimization in Information Retrieval Ruoyuan Gao and Chirag Shah
From a User Model for Query Sessions to Session Rank Biased Precision Aldo Lipani, Ben Carterette and Emine Yilmaz
Probabilistic Word Embeddings in Neural IR: A Promising Model That Does Not Work as Expected (For Now) Alberto Purpura, Marco Maggipinto, Gianmaria Silvello and Gian Antonio Susto
Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Michael Bendersky and Marc Najork
Learning a Better Negative Sampling Policy with Deep Neural Networks for Search Daniel Cohen, Scott Jordan and Bruce Croft
Using Principal Component Analysis to Better Understand Behavioral Measures and their Effects Jaime Arguello and Anita Crescenzi
Relevance Modeling with Multiple Query Variations Xiaolu Lu, Oren Kurland, Shane Culpepper, Nick Craswell and Ofri Rom
Unifying Explicit and Implicit Feedback for Rating Prediction and Ranking Recommendation Tasks Amir H. Jadidinejad, Craig Macdonald and Iadh Ounis
Anderson and Krathwohl’s Two-Dimensional Taxonomy Applied to Task Creation and Learning Assessment Kelsey Urgo, Jaime Arguello and Robert Capra
Unsupervised Story Comprehension with Hierarchical Encoder-Decoder Wang Bingning, Kang Liu, Zhixing Tian, Jun Zhao, Ting Yao, Qi Zhang and Jingfang Xu
JIGSAW: Structuring Text into Tables Dhruv Gupta and Klaus Berberich
SADHAN: Hierarchical Attention Networks to Learn Latent Aspect Embeddings for Fake News Detection Rahul Mishra and Vinay Setty
A Factored Relevance Model for Contextual Point-of-Interest Recommendation Anirban Chakraborty, Debasis Ganguly, Annalina Caputo and Séamus Lawless
Deep Learning of Human Information Foraging Behavior with a Search Engine Xi Niu and Xiangyu Fan
Integrated Learning of Features and Ranking Function in Information Retrieval Yifan Nie, Jiyang Zhang and Jian-Yun Nie
Modelling Dynamic Interactions Between Relevance Dimensions Sagar Uprety, Shahram Dehdashti, Lauren Fell, Peter Bruza and Dawei Song
Tangent-CFT: an Embedding model for Mathematical Formulas Behrooz Mansouri, Shaurya Rohatgi, Douglas Oard, Jian Wu, C. Lee Giles and Richard Zanibbi
Simulating CLIR Translation Resource Scarcity using High-resource Languages Hamed Bonab, James Allan and Ramesh Sitaraman

Accepted Short Papers

Title Authors
Neural Document Expansion with User Feedback Yue Yin, Chenyan Xiong, Cheng Luo and Zhiyuan Liu
A Study of Query Performance Prediction for Answer Quality Determination Haggai Roitman, Shai Erera and Guy Feigenblat
An Analysis of the Softmax Cross Entropy Loss for Learning-to-Rank with Binary Relevance Sebastian Bruch, Xuanhui Wang, Michael Bendersky and Marc Najork
A Comparative Analysis of Human and Automatic Query Variants Binsheng Liu, Nick Craswell, Xiaolu Lu, Oren Kurland and J. Shane Culpepper
An Assumption-Free Approach to the Dynamic Truncation of Ranked Lists Yen-Chieh Lien, Daniel Cohen and W. Bruce Croft
A Dataset and Baselines for e-Commerce Product Categorization Yiu-Chang Lin, Pradipto Das, Andrew Trotman and Surya Kallumadi
Generative Adversarial Networks in Precision Oncology Leandro von Werra, Marcel Schöngens, D. Ece Uzun and Carsten Eickhoff
DC³ – A Diagnostic Case Challenge Collection for Clinical Decision Support Carsten Eickhoff, Floran Gmehlin, Anu Patel, Jocelyn Boullier and Hamish Fraser
Category-Aware Location Embedding for Point-of-Interest Recommendation Hossein A. Rahmani, Mohammad Aliannejadi, Rasoul Mirzaei Zadeh, Mitra Baratchi, Mohsen Afsharchi and Fabio Crestani
Personal Knowledge Graphs: A Research Agenda Krisztian Balog and Tom Kenter
Investigating the Reliability of Click Models Jiaxin Mao, Zhumin Chu, Yiqun Liu, Min Zhang and Shaoping Ma
Why does this Entity matter? Support Passage Retrieval for Entity Retrieval Shubham Chatterjee and Laura Dietz
Unsupervised Context Retrieval for Long-tail Entities Dario Garigliotti, Dyaa Albakour, Miguel Martinez-Alvarez and Krisztian Balog
Listwise Neural Ranking Models Ali Montazeralghaem, Razieh Rahimi and James Allan
Generalising Kendall’s Tau for Noisy and Incomplete Preference Judgements Riku Togashi and Tetsuya Sakai
Exploratory Search Pipes with Scoped Facets Tim Gollub, Leon Hutans, Tanveer Al Jami and Benno Stein
SearchIE: A Retrieval Approach for Information Extraction Sheikh Muhammad Sarwar and James Allan
Performance Prediction for Non-Factoid Question Answering Helia Hashemi, Hamed Zamani and Bruce Croft
Sentence Retrieval for Entity List Extraction with a Seed, Context and Topic Sheikh Muhammad Sarwar, John Foley, Liu Yang and James Allan
Incorporating Hierarchical Domain Information to Disambiguate Very Short Queries Hamed Bonab, Mohammad Aliannejadi, John Foley and James Allan
Term Discrimination Value for Cross-Language Information Retrieval Ali Montazeralghaem, Razieh Rahimi and James Allan
A Multi-Source Collection of Event-Labeled News Documents Ida Mele and Fabio Crestani

Accepted Tutorials

Title Authors
Neural Learning to Rank using TensorFlow Ranking: A Hands-on Tutorial Rama Kumar Pasumarthi (Google AI), Sebastian Bruch (Google AI), Michael Bendersky (Google AI), Xuanhui Wang (Google AI)
Tutorial on Explainable Recommendation and Search Yongfeng Zhang (Rutgers University)

MAISoN 2019


  • Submission deadline: July 15, 2019 July 22, 2019 
  • Acceptance notification: August 2, 2019
  • Camera ready version: August 16, 2019


Papers submitted to this special issue for possible publication must be original and must not be under consideration for publication in any other journal or conference. Previously published or accepted conference papers must contain at least 30% new material to be considered for the special issue. All papers are to be submitted through the journal editorial submission system. At the beginning of the submission process in the submission system, authors need to select
S.I. : Mining Actionable Insights from Online User Generated Content
as the article type. All manuscripts must be prepared according to the journal publication guidelines which can also be found on the website provided above. Papers will be evaluated following the journal’s standard review process.


  • Marcelo G. Armentano, ISISTAN (CONICET-UNICEN), Argentina
  • Ebrahim Bagheri, Ryerson University, Canada
  • Julia Kiseleva, Microsoft AI, Seattle, Washington, United States
  • Frank Takes, University of Amsterdam, The Netherlands


We invite the submission of regular research papers as well as position papers. All submissions must be in English, in PDF format, and in ACM two-column format. Long paper submissions should not exceed 8 pages. Position papers should not exceed 4 pages. The LaTeX and Word templates of the ICTIR conference  available from the ACM website should also be used for your submission to the MAISoN workshop. All papers will be peer-reviewed by three reviewers. All submissions must be submitted through Easychair: