43rd International ACM SIGIR Conference on Research and Develop in Information Retrieval

SIGIR 2020

SIGIR’20 Program at a Glance(Time Zone: UTC+8)

Monday,July 27, 2020

Sunday,July 26

Saturday, July 25, 2020 SIGIR Summer School [Room: Epang Palace]
9:00-17:00 Tutorial
14:30-16:30 DC Student Talk Session I [Room: Xingqing Palace]
17:00-19:00 DC Student Talk Session II [Room: Xingqing Palace]
19:30-21:00 DC Student Talk Session III [Room: Xingqing Palace]
21:30-23:00 Social Event I (Student Event I) [Room: Weiyang Palace]
7:00-8:00 Social Event II (Student Event II) [Room: Weiyang Palace]
8:10-8:30 Welcome & Conference Opening [Room: Xi’an]
8:30-9:30 Keynote I (Playback is available after 10:00) [Room: Xi’an]
The Next Generation of Neural Networks
9:40
Session 1A
Session 1B[Room: Meituan][Room: Amazon]
11:40
Neural IR and Semantic Matching
Knowledge and Explainability
14:30-15:30 Keynote II (Playback is available after 16:00) [Room: Xi’an]
On Presuppositions of Machine Learning: A Meta Theory
15:40
Session 2A
Session 2B[Room: Meituan][Room: Amazon]
18:00
Knowledge for Personalization
User behavior and Experience
19:00-20:30 Social Event III (Women in IR – Session A) [Room: Weiyang Palace]

Session 1C[Room: Alibaba]
Graph-based Analysis

Industrial Session I[Room: Microsoft]

Session 2C[Room: Alibaba]
Evaluation

Industrial Session II[Room: Microsoft]

20:00-22:00 Short/Demo/TOIS paper session I [Room: Daming Palace]

Wednesday
July 29, 2020

Tuesday,
July 28, 2020

Session 3A
Session 3B
Session 3C
22:00[Room: Meituan][Room: Amazon][Room: Alibaba]
Bias and Fairness
Learning to Rank
Question Answering
24:00
8:30-9:30 Keynote III (Playback is available after 10:00) [Room: Xi’an]
Coopetition in IR Research
9:40
Session 4A
Session 4B
Session 4C[Room: Meituan][Room: Amazon][Room: Alibaba]
11:40
Query and Representation
Graph-based Recommendation
Neural Networks and Embedding
14:30-15:30 Keynote IV (Playback is available after 16:00) [Room: Xi’an]
Proof by Experimentation? Towards Better IR Research
15:40
Session 5A
Session 5B
Session 5C[Room: Meituan][Room: Amazon][Room: Alibaba]
17:40
Domain Specific Applications 1
Learning for Recommendation
Information Access and Filtering
20:00-22:00 Short/Demo/TOIS paper session II [Room: Daming Palace], Social Event IV (DEI Event) [Room: Weiyang Palace]
22:00
Session 6A
Session 6B
Session 6C[Room: Meituan][Room: Amazon][Room: Alibaba]
24:00
Neural Collaborative Filtering 1
Domain Specific Applications 2
Context-aware Modeling
0:00-1:30 Social Event V (Women in IR – Session B) [Room: Weiyang Palace]
8:30-9:30 Keynote V (Playback is available after 10:00) [Room: Xi’an]
From Information to Assistance
9:40
Session 7A
Session 7B[Room: Meituan][Room: Amazon]
11:40
Conversation and Interactive IR
Text Classification and Transfer Learning
14:30-15:30 Keynote VI (Playback is available after 16:00) [Room: Xi’an]
How Deep Learning Works for Information Retrieval
15:40
Session 8A
Session 8B[Room: Meituan][Room: Amazon]
17:40
Domain Specific Retrieval Tasks
Multi-modal Retrieval and Ranking

Industrial Session III[Room: Microsoft]

Industrial Session IV[Room: Microsoft]

Session 7C[Room: Alibaba]
Neural Collaborative Filtering 2

Industrial Session V[Room: Microsoft]

Session 8C[Room: Alibaba]
Sequential Recommendation

Industrial Session VI[Room: Microsoft]

21:00-24:00 Conference Closing and Business Meeting [Room: Xi’an]Thursday, July 30, 2020 Workshops

Virtual Conference Portal is available at https://sigir-schedule.baai.ac.cn/.

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SIGIR’20 Keynote Talks
The Next Generation of Neural Networks
Prof. Geoffrey Hinton
Google Research & Vector Institute
CanadaTime: July 27, 8:30-9:30 (Time Zone: UTC+8)
Room: Xi’an

Abstract
The most important unsolved problem with artificial neural networks is how to do unsupervised learning
as effectively as the brain. There are currently two main approaches to unsupervised learning. In thefirst approach, exemplified by BERT and Variational Autoencoders, a deep neural network is used toreconstruct its input. This is problematic for images because the deepest layers of the network need to
encode the fine details of the image. An alternative approach, introduced by Becker and Hinton in 1992,
is to train two copies of a deep neural network to produce output vectors that have high mutual
information when given two different crops of the same image as their inputs. This approach was
designed to allow the representations to be untethered from irrelevant details of the input.
The method of optimizing mutual information used by Becker and Hinton was flawed (for a subtle
reason that I will explain) so Pacannaro and Hinton replaced it by a discriminative objective in which one
vector representation must select a corresponding vector representation from among many
alternatives. With faster hardware, contrastive learning of representations has recently become very
popular and is proving to be very effective, but it suffers from a major flaw: To learn pairs ofrepresentation vectors that have N bits of mutual information we need to contrast the correct
corresponding vector with about 2^N incorrect alternatives. I will describe a novel and effective way of
dealing with this limitation. I will also show that this leads to a simple way of implementing perceptual
learning in cortex.
Biography
Geoffrey Hinton received his PhD in Artificial Intelligence from Edinburgh in 1978. After five years as afaculty member at Carnegie-Mellon he became a fellow of the Canadian Institute for Advanced Research
and moved to the Department of Computer Science at the University of Toronto where he is now an
Emeritus Distinguished Professor. He is also a Vice President & Engineering Fellow at Google and Chief
Scientific Adviser of the Vector Institute.
He was one of the researchers who introduced the backpropagation algorithm and the first to use
backpropagation for learning word embeddings. His other contributions to neural network research
include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts,
variational learning and deep learning. His research group in Torontomademajor breakthroughs in deeplearning that revolutionized speech recognition and object classification.

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SIGIR 2020

On Presuppositions of Machine Learning: A Meta Theory
Prof. Zongben Xu
Xi’an Jiaotong University & Pazhou Lab
China
Time: July 27, 14:30-15:30 (Time Zone: UTC+8)
Room: Xi’an

Abstract
Machine learning (ML) has been run and applied by premising a series of presuppositions, which
contributes both the great success of AI and the bottleneck of further development of ML. These
presuppositions include (i) the independence assumption of loss function on dataset (Hypothesis I); (ii)the large capacity assumption on hypothesis space including solution (Hypothesis II); (iii) thecompleteness assumption of training data with high quality (Hypothesis III); and (iv) the Euclidean
assumption on analysis framework and methodology (Hypothesis IV).
We report, in this presentation, the effort and advances made by my group on how to break throughthese presuppositions of ML and drive ML development. For Hypothesis I, we introduce the noisemodeling principle to adaptively design the loss function of ML, according to the distribution of data
samples, which provides then a general way to robustlize any ML implementation. For Hypothesis II, we
propose the model driven deep learning approach to define the smallest hypothesis space of deepneural networks (DNN), which yields not only the very efficient deep learning, but also a novel way of
DNN design, interpretation and connection with the traditional optimization based approach. For
Hypothesis III, we develop the axiomatic curriculum learning framework to learn the patterns from an
incomplete dataset step by step and from easy to difficult, which then provides feasible ways to tackle
very complex incomplete data sets. Finally, For Hypothesis IV, we introduce Banach space geometry ingeneral, and XU-Roach theorem in particular, as a possibly useful tool to conduct non-Euclidean analysis
of ML problems. In each case, we present the idea, principles, application examples and literatures.
Biography
Zongben Xu, received his PhD degree in Mathematics in 1987 from Xi’an Jiaotong University, China. In
1988, he was a postdoctoral Research Fellow in the Department of Mathematics, The University ofStrathclyde.
He worked as a Research Fellow in the Department of Computer Science and Engineering from 1992 to
1994, and 1996 to 1997, at The Chinese University of Hong Kong; a visiting professor in the University ofEssex in 2001, and Napoli University in 2002. He has been with the School of Mathematics and Statistics,
Xi’an Jiaotong University since 1982, where he served as a professor of mathematics and computerscience, Dean of Sciences (1997-2003), VP of the university (2003-2014) and Chief Scientist of National
Basic Research Program of China (973 Project). He is currently the director of Pazhou Lab, Guangzhou

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and the National Lab for Big Data Analytics, Xi’an. He is also the Dean of Xi’an Academy of Mathematics
and Mathematical Technology.
Professor Xu makes several important services for government and professional societies currently,
including Consultant member of National Big Data Development Commission, the New Generation AI
Development Commission and the National Natural Science Foundation of China. He is VP of Industrial
and Applied Mathematics Society of China (CSIAM), the director of Big Data and AI Committee of CSIAM.
He is also the co- Editor-in-chief of Journal of Big Data Analytics and Textbooks Series on Data Scienceand Big Data Technology (Higher Education Press of China).
Professor Xu has published over 280 academic papers on nonlinear functional analysis, optimization,
machine learning and big data research, most of which are in international journals. His current researchinterests include mathematical theory and fundamental algorithms for big data Analysis, machinelearning and data Science. Professor Xu has gotten many academic awards, say, the National Natural
Science Award (2007), the National Scientific and Technological Advance Award (2011) of China, CSIAMSu Buchin Applied Mathematics Award (2008) and Tan Kah Kee Science Award (in Information
Technology Science, 2018). He delivered a 45 minutes talk at the International Congress ofMathematicians (ICM 2010) upon the invitation of the congress committee. He was elected as amember of Chinese Academy of Science in 2011.

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SIGIR 2020

Coopetition in IR Research
Dr. Ellen M. Voorhees
National Institute for Standards and Technology
U.S.A.
Time: July 28, 8:30-9:30 (Time Zone: UTC+8)
Room: Xi’an

Abstract
Coopetitions are activities in which competitors cooperate for a common good. Community evaluations
such as the Text REtrieval Conference (TREC) are prototypical examples of coopetitions in information
retrieval (IR) and have now been part of the field for almost thirty years. This longevity and theproliferation of shared evaluation tasks suggest that, indeed, the net impact of community evaluations is
positive. But what are these benefits, and what are the attendant costs?
This talk will use TREC tracks as case studies to explore the benefits and disadvantages of different
evaluation task designs. Coopetitions can improve state-of-the-art effectiveness for a retrieval task by
establishing a research cohort and constructing the infrastructure—including problem definition, testcollections, scoring metrics, and research methodology—necessary to make progress on the task. They
can also facilitate technology transfer and amortize the infrastructure costs. The primary danger ofcoopetitions is for an entire research community to overfit to some peculiarity of the evaluation task.
This risk can be minimized by building multiple test sets and regularly updating the evaluation task.
Biography
Ellen Voorhees is a Senior Research Scientist at the US National Institute of Standards and Technology(NIST). Her primary responsibility at NIST is to manage the Text REtrieval Conference (TREC) project, aproject that develops the infrastructure required for largescale evaluation of search engines and otherinformation access technology. Voorhees’ research focuses on developing and validating appropriate
evaluation schemes to measure system effectiveness for diverse user tasks.
Voorhees received a B.Sc. in computer science from the Pennsylvania State University, and M.Sc. and
Ph.D. degrees in computer science from Cornell University. Prior to joining NIST she was a Senior
Member of Technical Staff at Siemens Corporate Research in Princeton, NJ where her work on intelligent
agents applied to information access resulted in three patents.
Voorhees is a fellowof the ACM, a member of AAAI, and has been elected as a fellow of theWashington
Academy of Sciences. She has published numerous articles on information retrieval techniques andevaluation methodologies and serves on the review boards of several journals and conferences.

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Proof by Experimentation? Towards Better IR Research
Prof. Norbert Fuhr
University of Duisburg-Essen
Germany
Time: July 28, 14:30-15:30 (Time Zone: UTC+8)
Room: Xi’an

Abstract
The current fight against the COVID-19 pandemic illustrates the importance of proper scientificmethods: Besides fake news lacking any factual evidence, reports on clinical trials with various drugs
often yield contradicting results; here, only a closer look at the underlying empirical methodology canhelp in forming a clearer picture.
In IR research, empirical foundation in the form of experiments plays an important role. However, themethods applied often are not at the level of scientific standards that hold in many other disciplines, as
IR experiments are frequently flawed in several ways: Measures like MRR or ERR are invalid bydefinition, and MAP is based on unrealistic assumptions about user behaviour; computing relative
improvements of arithmetic means is statistical nonsense; test hypotheses often are formulated after
the experiment has been carried out; multiple hypotheses are tested without correction; many
experiments are not reproducible results or are compared to weak baselines [1, 6]; frequent reuse of
the same test collections yields random results [2]; authors (and reviewers) believe that experiments
prove the claims made. Methods for overcoming these problems have been pointed out [5], but are stillwidely ignored.
However, even when experimental results have been achieved via proper methods, this only solves theissue of internal validity. The problem of external validity has hardly been addressed in IR so far. Just
having empirical results for a handful or test collections does not enable us to make any statements on
how far we can generalise these observations. So we should put more emphasis on understanding why
certain methods work (or don’t work) under certain circumstances - instead of looking at improvements
at the third or fourth decimal place. This would allow us to make more generally valid statements, andbe a first step towards being able to predict performance for new collections
To conclude, better research in IR can only be achieved by• Enforcing rigorous experimental methodology at our top venues.
• Establishing leaderboards and carrying out metastudies for monitoring the actual scientificprogress in our field.
• Understanding should be valued higher than raw performance.
• Ultimately, research should aim more at performance prediction than at performancemeasurement.

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Biography
Norbert Fuhr holds a PhD (Dr.) in Computer Science from the Technical University of Darmstadt, which
he received in 1986. He became Associate Professor in the computer science department of the
University of Dortmund in 1991 and was appointed Full Professor for computer science at the Universityof Duisburg-Essen in 2002. His past research dealt with topics such as probabilistic retrieval models, theintegration of IR and databases, retrieval in distributed digital libraries and XML documents, and user
friendly retrieval interfaces. His current research interests are models for interactive retrieval, social
media retrieval, and evaluation methodology Norbert Fuhr has served as PC member and program chairof major conferences in IR and digital libraries, and on the editorial boards of several journals in theseareas. In 2012, he received the Gerald Salton Award of ACM-SIGIR.

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From Information to AssistanceDr. Elizabeth F. Churchill
GoogleU.S.A.
Time: July 29, 8:30-9:30 (Time Zone: UTC+8)
Room: Xi’an

Abstract“Knowledge is of two kinds. We know a subject ourselves, or we know where we can find information
upon it. When we enquire into any subject, the first thing we have to do is to know what books havetreated of it. This leads us to look at catalogues, and at the backs of books in libraries.”– Samuel Johnson (Boswell’s Life of Johnson)
When Johnson was writing this, said libraries were very exclusive, inaccessible tomost. When I was
growing up the librarywas a favorite place to find informationwith the help of expert assistants, trained
librarians. Nowadays, while libraries are still one of my favorite institutions, we have powerful digital
information search, retrieval, and assemblage services, bundled into easily accessible tools at our
fingertips.
As information proliferates and human information needs remain high, information retrieval willcontinue to be a central area of investigation. We will also need better and better tools to access,assemble, and represent that information in ways that can be understood and applied-tools that ensure
information turns into knowledge that is useful and used.
In this talk, I will focus on how people find information, and how the tools we build aid in that finding.
Using case studies, I will outline some that remain challenging, and offer some case studies and edgecases where more work is needed. I will share thoughts on how emerging assistant devices and services
are and are not meeting the challenge of becoming expert information assistants.
Biography
Elizabeth Churchill is a Director of UX at Google. She is also the Executive Vice President of the
Association of ComputingMachinery (ACM), a member of the ACM’s CHI Academy, and an ACM Fellow,
Distinguished Scientist, and Distinguished Speaker. With a background in psychology, Artificial
Intelligence and Cognitive Science, she draws on social, computer, engineering and data sciences tocreate innovative digital tools, applications, and services. She has built research teams at Google, eBay,
Yahoo, PARC and FujiXerox. She holds a PhD from the University of Cambridge and honorary doctorates
from the University of Sussex, and the University of Stockholm. In 2016, she received the Citris-Banatao
Institute Athena Award for Executive Leadership.

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How Deep Learning Works for Information Retrieval
Prof. Dacheng Tao
The University of Sydney
Australia
Time: July 29, 14:30-15:30 (Time Zone: UTC+8)
Room: Xi’an

Abstract
Information retrieval (IR) is the science of search, the search of user query relevant pieces of
information from a collection of unstructured resources. Information in this context includes text,
imagery, audio, video, xml, program, and metadata. The journey of an IR process begins with a user
query sent to the IR system which encodes the query, compares the query with the available resources,
and returns the most relevant pieces of information. Thus, the system is equipped with the ability tostore, retrieve and maintain information.
In the early era of IR, the whole process was completed using handcrafted features and ad-hoc
relevance measures. Later, principled frameworks for relevance measure were developed with statisticallearning as a basis. Recently, deep learning has proven essential to the introduction of more
opportunities to IR. This is because data-driven features combined with datadriven relevance measures
can effectively eliminate the human bias in either feature or relevance measure design.
Deep learning has shown its significant potential to transform IR evidenced by abundant empiricalresults. However, we continue to strive to gain a comprehensive understanding of deep learning. This isdone by answering questions such as why deep structures are superior to shallow structures, how skip
connections affect a model’s performance, uncovering the potential relationship between some of thehyper-parameters and a model’s performance, and exploring ways to reduce the chance for deepmodels to be fooled by adversaries. Answering such questions can help design more effective deepmodels and devise more efficient schemes for model training.
Biography
Dacheng Tao is Professor of Computer Science and ARC Laureate Fellow in the School of Computer
Science and the Faculty of Engineering, and the Inaugural Director of the UBTECH Sydney Artificial
Intelligence Centre, at The University of Sydney. His research results in artificial intelligence have
expounded in one monograph and 200+ publications at learning journals and conferences, such as IEEE
TPAMI, AAAI, IJCAI, NeurIPS, ICML, CVPR, ICCV, ECCV, ICDM, and KDD, with several best paper awards.
He received the 2018 IEEE ICDM Research Contributions Award and the 2015 Australian Museum
Scopus-Eureka prize. He is a Fellow of the IEEE, the ACM and the Australian Academy of Science.

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Industry Track Invited Talks
Large-scale Multi-modal Search and QA at Alibaba
Dr. Rong Jin
DAMO Academy, Alibaba Group
China
Time: July 27, 9:40-10:40 (Time Zone: UTC+8)
Room: Microsoft

Abstract
In this talk, we will present ongoing efforts of Alibaba on large-scale multi-modal search ad questionanswering, including image search, video search, cross lingual retrieval, and QA based on machinereading and comprehension (MRC). We will also share in this talk the core technologies that are crucial
to the development of the related technologies, including representation learning, data augmentation,and robust learning.
Biography
Rong Jin is currently an associate director of DAMO academy at Alibaba, leading the research anddevelopment of state-of-the-art AI technologies. Before joining Alibaba, he was a faculty member of theComputer and Science Engineering Dept. at Michigan State University from 2003 to 2015. His research is
focused on statistical machine learning and its application to large-scale data analysis. He published over
300 technique papers, mostly on the top conferences and prestigious journals. He is an associate editorof IEEE Transaction at Pattern Analysis and Machine Intelligence (TPAMI) and ACM Transaction at
Knowledge Discovery from Data. Dr. Jin holds Ph.D. in Computer Science from Carnegie Mellon
University. He received the NSF career award in 2006, and the best paper award from COLT in 2012.

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The New TREC Track on Podcast Search and Summarization
Dr. Rosie Jones
Spotify
U.S.A.
Time: July 28, 9:40-10:40 (Time Zone: UTC+8)
Room: Microsoft

Abstract
Podcasts are exploding in popularity. As this medium grows, it becomes increasingly important tounderstand the content of podcasts (e.g. what exactly is being covered, by whom, and how?), and how
we can use this to connect users to shows that align with their interests. Given the explosion of newmaterial, how do listeners find the needle in the haystack, and connect to those shows or episodes thatspeak to them? Furthermore, once they are presented with potential podcasts to listen to, how can theydecide if this is what they want?
To move the needle forward more rapidly toward this goal, we’ve introduced the Spotify Podcasts
Dataset [1] and TREC shared task [2]. This dataset represents the first large-scale set of podcasts, withtranscripts, released to the research community. The accompanying shared task is part of the TREC 2020
Conference, run by the US National Institute of Standards and Technology. The challenge is planned to
run for several years, with progressively more demanding tasks: this first year, the challenge involves asearch-related task and a task to automatically generate summaries, both based on transcripts of theaudio.
In this talk I will describe the task and dataset, outlining how the dataset is orders of magnitude larger
than previous spoken document datasets, and how the tasks take us beyond previous shared tasks bothin spoken document retrieval and NLP.
Biography
Rosie Jones is a Director of Research at Spotify. Her lab conducts research on language technologies,
with applications including podcast search and recommendation, and voice understanding. Prior tojoining Spotify, she was a Principal Scientist at Microsoft working on conversational agents. She has also
worked as Director of Computational Advertising at Akamai, and as a Principal Scientist at Yahooworking on online advertising and search. She is a senior member of the ACM and is a PC co-chair for
SIGIR 2021.

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SIGIR’20 Main Conference Program
Monday, July 27, 2020
(Time Zone: UTC+8)
Welcome & Conference Opening (July 27, 8:10-8:30)
Room: Xi’an
Welcome: General Chairs
Program Introduction: PC Chairs
Keynote I (July 27, 8:30-9:30; playback is available after 10:00)
Chair: Jimmy Huang and Yi Chang
Room: Xi’an
Title: The Next Generation of Neural Networks
Speaker: Geoffrey Hinton, Google Research & Vector InstituteSession 1A (July 27, 9:40-11:40)
Neural IR and Semantic Matching
Chair: Hamed Zamani (Microsoft)
Room: Meituan• Training Effective Neural CLIR by Bridging the Translation Gap. Hamed Bonab, Sheikh Muhammad
Sarwar, and James Allan• A Quantum Interference Inspired Neural Matching Model for Ad-hoc Retrieval. Yongyu Jiang, Peng
Zhang, Hui Gao, and Dawei Song• A Deep Recurrent Survival Model for Unbiased Ranking. Jiarui Jin, Yuchen Fang, Weinan Zhang, Kan
Ren, Guorui Zhou, Jian Xu, Yong Yu, Jun Wang, Xiaoqiang Zhu, and Kun Gai• ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT. Omar
Khattab and Matei Zaharia• Efficient Document Re-Ranking for Transformers by Precomputing Term Representations. Sean
MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder• A Reinforcement Learning Framework for Relevance Feedback, Ali Montazeralghaem, Hamed
Zamani, and James Allan
Session 1B (July 27, 9:40-11:40)
Knowledge and Explainability
Chair: Vanessa Murdock (Amazon)
Room: Amazon• Fairness-Aware Explainable Recommendation over Knowledge Graphs. Zuohui Fu, Yikun Xian,
Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah,
Yongfeng Zhang, and Gerard de Melo
• Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a
Heterogeneous View. Jibing Gong, Shen Wang, Jinlong Wang, Hao Peng, Wenzheng Feng, Dan
Wang, Yi Zhao, Huanhuan Li, Jie Tang, and P. Yu• Sequential Recommendation with Self-attentive Multi-adversarial Network. Ruiyang Ren, Zhaoyang
Liu, Yaliang Li, Wayne Xin Zhao, Hui Wang, Bolin Ding, and Ji-Rong Wen

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• MVIN: Learning multiview items for recommendation. Chang-You Tai, Meng-Ru Wu, Yun-Wei Chu,
Shao-Yu Chu, and Lun-Wei Ku• Make It a CHORUS: Context- and Knowledge-aware Item Modeling for Recommendation. ChenyangWang, Min Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma• Evolutionary Product Description Generation: A Dynamic Fine-Tuning Approach Leveraging User
Click Behavior. Yongzhen Wang, Jian Wang, Heng Huang, Hongsong Li, and Xiaozhong Liu
Session 1C (July 27, 9:40-11:40)
Graph-based Analysis
Chair: Qiaozhu Mei (University of Michigan)
Room: Alibaba• Pairwise View Weighted Graph Network for View-based 3D Model Retrieval. Zan Gao, Yin-Ming Li,
Wei-Li Guan, Wei-Zhi Nie, Zhi-Yong Cheng, and An-An Liu• Detecting User Community in Sparse Domain via Cross-Graph Pairwise Learning. Zheng Gao,
Hongsong Li, Zhuoren Jiang, and Xiaozhong Liu• BiANE: Bipartite Attributed Network Embedding. Wentao Huang, Yuchen Li, Yuan Fang, Ju Fan, andHongxia Yang
• Hierarchical Fashion Graph Network for Personalised Outfit Recommendation. Xingchen Li, Xiang
Wang, Xiangnan He, Long Chen, Jun Xiao, and Tat-Seng Chua
• Global Context Enhanced Graph Nerual Networks for Session-based Recommendation. ZiyangWang, Wei Wei, Cong Gao, Xiaoli Li, Xianling Mao, and Minghui Qiu• Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning. Sijin
Zhou, Xinyi Dai, Haokun Chen, Weinan Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, and Yong Yu
Industrial Session I (July 27, 9:40-11:40)
Chair: Weinan Zhang (Shanghai Jiao Tong University)
Room: Microsoft• Invited talk: Large-scale Multi-modal Search and QA at Alibaba. Rong Jin• User Behavior Retrieval for Click-Through Rate Prediction. Jiarui Qin, Weinan Zhang, Xin Wu, Jiarui
Jin, Yuchen Fang, and Yong Yu• How Airbnb Tells You Will Enjoy Sunset Sailing in Barcelona? Recommendation in a Two-Sided Travel
Marketplace. Liang Wu and Mihajlo Grbovic
Break (12:00-14:30)
Virtual discussion rooms available
Keynote II (July 27, 14:30-15:30; playback is available after 16:00)
Chair: Yi Chang and Xueqi Cheng
Room: Xi’an
Title: On Presuppositions of Machine Learning: A Meta Theory
Speaker: Zongben Xu, Xi’an Jiaotong University & Pazhou Lab
Session 2A (July 27, 15:40-18:00)
Knowledge for Personalization
Chair: Imed Zitouni (Google)
Room: Meituan

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• Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation. Chong Chen, Min
Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma• AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in
CTR Prediction. Bin Liu, Niannan Xue, Huifeng Guo, Ruiming Tang, Stefanos Zafeiriou, Xiuqiang He,
and Zhenguo Li• KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential Recommendation. Pengfei
Wang, Yu Fan, Long Xia, Wayne Xin Zhao, Shaozhang Niu, and Jimmy Huang• CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems. Ze Wang, Lin
Guangyan, Huobin Tan, Qinghong Chen, and Xiyang Liu• CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network. Cheng
Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, and Aixin Sun• Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs.
Kangzhi Zhao, Xiting Wang, Yuren Zhang, Li Zhao, Zheng Liu, Chunxiao Xing, and Xing Xie• Incorporating Scenario Knowledge into A Unified Fine-tuning Architecture for Event Representation.
Jianming Zheng, Fei Cai, and Honghui Chen
Session 2B (July 27, 15:40-18:00)
User Behavior and Experience
Chair: Suzan Verberne (Leiden University)
Room: Amazon• Ranking-Incentivized Quality Preserving Content Modification. Gregory Goren, Oren Kurland, Moshe
Tennenholtz, and Fiana Raiber• On Understanding Data Worker Interaction Behaviors. Lei Han, Tianwa Chen, Gianluca Demartini,
Marta Indulska, and Shazia Sadiq• Creating a Children-Friendly Reading Environment via Joint Learning of Content and Human
Attention. Guoxiu He, Yangyang Kang, Zhuoren Jiang, Jiawei Liu, Changlong Sun, Xiaozhong Liu, andWei Lu• Octopus: Comprehensive and Elastic User Representation for the Generation of Recommendation
Candidates. Zheng Liu, Junhan Yang, Jianxun Lian, Defu Lian, and Xing Xie• The Cortical Activity of Graded Relevance. Zuzana Pinkosova, William McGeown, and Yashar
Moshfeghi• Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback. Yuta Saito• Beyond User Embedding Matrix: Learning to Hash for Modeling Large-Scale Users in
Recommendation. Shaoyun Shi, Weizhi Ma, Min Zhang, Yongfeng Zhang, Xinxing Yu, Houzhi Shan,
Yiqun Liu, and Shaoping Ma
Session 2C (July 27, 15:40-18:00)
Evaluation
Chair: Mark Sanderson (RMIT University)
Room: Alibaba• Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations. Krisztian
Balog and Filip Radlinski• Bayesian Inferential Risk Evaluation on Multiple IR Systems. Rodger Benham, Ben Carterette, J.
Shane Culpepper, and Alistair Moffat• How to Measure the Reproducibility of System-oriented IR Experiments. Timo Breuer, Nicola Ferro,
Norbert Fuhr, Maria Maistro, Tetsuya Sakai, Philipp Schaer, and Ian Soboroff• Good Evaluation Measures based on Document Preferences. Tetsuya Sakai and Zhaohao Zeng

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• Preference-based Evaluation Metrics for Web Image Search. Xiaohui Xie, Jiaxin Mao, Yiqun Liu,
Maarten de Rijke, Haitian Chen, Min Zhang, and Shaoping Ma• Models Versus Satisfaction: Towards a Better Understanding of Evaluation Metrics. Fan Zhang,
Jiaxin Mao, Yiqun Liu, Xiaohui Xie, Weizhi Ma, Min Zhang, and Shaoping Ma• Cascade or Recency: Constructing Better Evaluation Metrics for Session Search. Fan Zhang, Jiaxin
Mao, Yiqun Liu, Weizhi Ma, Min Zhang, and Shaoping Ma
Industrial Session II (July 27, 15:40-18:00)
Chair: Mounia Lalmas (Spotify)
Room: Microsoft• Efficient and Effective Query Auto-Completion. Simon Gog, Giulio Ermanno Pibiri, and Rossano
Venturini• ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation. Yufei
Feng, Binbin Hu, Fuyu Lv, Qingwen Liu, Zhiqiang Zhang, and Wenwu Ou• Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer
Network. Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Bo Zhang, and Liefeng Bo• Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate
Prediction. Hong Wen, Jing Zhang, Yuan Wang, Fuyu Lv, Wentian Bao, Quan Lin, and Keping Yang
• Automated Embedding Size Search in Deep Recommender Systems. Haochen Liu, Xiangyu Zhao,
Chong Wang, Xiaobing Liu, and Jiliang Tang
Short/Demo/TOIS paper session I (July 27, 20:00-22:00)
Room: Daming Palace
Virtual discussion rooms available
Session 3A (July 27, 22:00-24:00)
Bias and Fairness
Chair: Ricardo Baeza-Yates (Northeastern University)
Room: Meituan• Operationalizing the Legal Principle of Data Minimization for Personalization. Asia J. Biega, Peter
Potash, Hal Daumé III, Fernando Diaz, and Michèle Finck• Learning Personalized Risk Preferences for Recommendation. Yingqiang Ge, Shuyuan Xu, ShuchangLiu, Zuohui Fu, Fei Sun, and Yongfeng Zhang• Certifiable Robustness to Discrete Adversarial Perturbations for Factorization Machines. Yang Liu,
Xianzhuo Xia, Liang Chen, Xiangnan He, Carl Yang, and Zibin Zheng• Controlling Fairness and Bias in Dynamic Ranking. Marco Morik, Ashudeep Singh, Jessica Hong, andThorsten Joachims• Can the Crowd Identify Misinformation Objectively? The Effects of Judgments Scale and Assessor's
Bias. Kevin Roitero, Michael Soprano, Shaoyang Fan, Damiano Spina, Stefano Mizzaro, and Gianluca
Demartini• Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems. Ziwei
Zhu, Jianling Wang, and James Caverlee
Session 3B (July 27, 22:00-24:00)
Learning to Rank
Chair: Hang Li (Bytedance)
Room: Amazon

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• What Makes a Top-Performing Precision Medicine Search Engine? Tracing Main System Features in a
Systematic Way. Erik Faessler, Michel Oleynik, and Udo Hahn
• Accelerated Convergence for Counterfactual Learning to Rank. Rolf Jagerman and Maarten de Rijke• DVGAN: A Minimax Game for Search Result Diversification Combining Explicit and Implicit Features.
Jiongnan Liu, Zhicheng Dou, Xiaojie Wang, Shuqi Lu, and Ji-Rong Wen• Policy-Aware Unbiased Learning to Rank for Top-k Rankings. Harrie Oosterhuis and Maarten de Rijke• SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval. Liang Pang, Jun
Xu, Qingyao Ai, Yanyan Lan, Xueqi Cheng, and Ji-Rong Wen• Reinforcement Learning to Rank with Pairwise Policy Gradient. Jun Xu, Zeng Wei, Long Xia, Yanyan
Lan, Dawei Yin, Xueqi Cheng, and Ji-Rong Wen
Session 3C (July 27, 22:00-24:00)
Question Answering
Chair: Jochen L. Leidner (Refinitiv Labs/University of Sheffield)
Room: Alibaba• Humor Detection in Product Question Answering Systems. Elad Kravi, David Carmel, and Yftah Ziser• Training Curricula for Open Domain Answer Re-Ranking. Sean MacAvaney, Franco Maria Nardini,
Raffaele Perego, Nicola Tonellotto, Nazli Goharian, and Ophir Frieder• Open-Retrieval Conversational Question Answering. Chen Qu, Liu Yang, Cen Chen, Minghui Qiu, W.
Bruce Croft, and Mohit Iyyer• Learning to Ask Screening Questions for Job Postings. Baoxu Shi, Shan Li, Jaewon Yang, Mustafa
Emre Kazdagli, and Qi He• Match^2: A Matching over Matching Model for Similar Question Identification. Zizhen Wang, Yixing
Fan, Jiafeng Guo, Liu Yang, Ruqing Zhang, Yanyan Lan, Xueqi Cheng, Hui Jiang, and Xiaozhao Wang
• Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling. Wenxuan
Zhang, Yang Deng, and Wai Lam

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Tuesday, July 28, 2020
(Time Zone: UTC+8)
Break (July 28, 0:00-6:00)
Virtual discussion rooms available
Keynote III (July 28, 8:30-9:30; playback is available after 10:00)
Chair: Jimmy Huang and Yiqun Liu
Room: Xi’an
Title: Coopetition in IR Research
Speaker: Ellen M. Voorhees, National Institute for Standards and Technology
Session 4A (July 28, 9:40-11:40)
Query and Representation
Chair: Rob Capra (University of North Carolina)
Room: Meituan• ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail
Performance. Zhihong Chen, Rong Xiao, Chenliang Li, Gangfeng Ye, Haochuan Sun, and Hongbo
Deng• Table Search Using a Deep Contextualized Language Model. Zhiyu Chen, Mohamed Trabelsi, Jeff
Heflin, Yinan Xu, and Brian Davison• Convolutional Embedding for Edit Distance. Xinyan Dai, Xiao Yan, Kaiwen Zhou, Yuxuan Wang, Han
Yang, and James Cheng• ASiNE: Adversarial Signed Network Embedding. Yeon-Chang Lee, Nayoun Seo, Sang-Wook Kim, andKyungsik Han• Efficient Graph Query Processing over Geo-Distributed Datacenters. Ye Yuan, Delong Ma, Zhenyu
Wen, Yuliang Ma, Guoren Wang, and Lei Chen
• Spatio-Temporal Dual Graph Attention Network for Query-POI Matching. Zixuan Yuan, Hao Liu,
Yanchi Liu, Denghui Zhang, Fei Yi, Nengjun Zhu, and Hui Xiong
Session 4B (July 28, 9:40-11:40)
Graph-based Recommendation
Chair: Chirag Shah (University of Washington)
Room: Amazon• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. Xiangnan
He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang
• GAME: Learning Graphical and Attentive Multi-view Embeddings for Occasional Group
Recommendation. Zhixiang He, Chi-Yin Chow, and Jia-Dong Zhang• Multi-behavior Recommendation with Graph Convolution Networks. Bowen Jin, Chen Gao, Xiangnan
He, Yong Li, and Depeng Jin• GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation.
Ruihong Qiu, Hongzhi Yin, Zi Huang, and Tong Chen
• Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network
Approach. Le Wu, Yonghui Yang, Kun Zhang, Richang Hong, Yanjie Fu, and Meng Wang
• GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster
Identification. Shijie Zhang, Hongzhi Yin, Tong Chen, Nguyen Quoc Viet Hung, Zi Huang, and Lizhen
Cui

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Session 4C (July 28, 9:40-11:40)
Neural Networks and Embedding
Chair: Doug Oard (University of Maryland)
Room: Alibaba• Using Phoneme Representations to Build Predictive Models Robust to ASR Errors. Anjie Fang,
Simone Filice, Nut Limsopatham, and Oleg Rokhlenko• Knowledge Enhanced Personalized Search. Shuqi Lu, Zhicheng Dou, Chenyan Xiong, Xiaojie Wang,and Ji-Rong Wen• Learning Dynamic Node Representations with Graph Neural Networks. Yao Ma, Ziyi Guo, Zhaochun
Ren, Jiliang Tang, and Dawei Yin• An Eye Tracking Study of Web Search by People with and without Dyslexia. Srishti Palani, Adam
Fourney, Shane Williams, Kevin Larson, Irina Spiridonova, and Meredith Ringel Morris• DGL-KE: Training knowledge graph embeddings at scale. Da Zheng, Xiang Song, Chao Ma, ZeyuanTan, Zihao Ye, Hao Xiong, Zheng Zhang, and George Karypis• Neural Interactive Collaborative Filtering. Lixin Zou, Long Xia, Yulong Gu, Weidong Liu, Dawei Yin,
Jimmy Huang, and Xiangyu Zhao
Industrial Session III (July 28, 9:40-11:40)
Chair: Vanessa Murdock (Amazon)
Room: Microsoft• Invited talk: The New TREC Track on Podcast Search and Summarization. Roise Jones• Think Beyond the Word: Understanding the Implied Textual Meaning by Digesting Context, Local,
and Noise. Guoxiu He, Zhe Gao, Zhuoren Jiang, Yangyang Kang, Changlong Sun, Xiaozhong Liu, andWei Lu• Robust Layout-aware IE for Visually Rich Documents with Pre-trained Language Models. Mengxi
Wei, Yifan He, and Qiong Zhang
Break (12:00-14:30)
Virtual discussion rooms available
Keynote IV (July 28, 14:30-15:30; playback is available after 16:00)
Chair: Jimmy Huang and Xueqi Cheng
Room: Xi’an
Title: Proof by Experimentation? Towards Better IR Research
Speaker: Norbert Fuhr, University of Duisburg-Essen
Session 5A (July 28, 15:40-17:40)
Domain Specific Applications 1
Chair: Elad Yom-Tov (Microsoft Research)
Room: Meituan• Fashion Compatibility Modeling through a Multi-modal Try-on-guided Scheme. Xue Dong, Jianlong
Wu, Xuemeng Song, Hongjun Dai, and Liqiang Nie• Spatial Object Recommendation with Hints: When Spatial Granularity Matters. Hui Luo, Jingbo Zhou,
Zhifeng Bao, Shuangli Li, J. Shane Culpepper, Haochao Ying, Hao Liu, and Hui Xiong• Product Bundle Identification using Semi-Supervised Learning. Hen Tzaban, Ido Guy, Asnat
Greenstein-Messica, Arnon Dagan, Lior Rokach, and Bracha Shapira

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• Coding Electronic Health Records with Adversarial Reinforcement Path Generation. Shanshan Wang,
Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jian-Yun Nie, Jun Ma, and Maarten de Rijke• Degree-Aware Alignment for Entities in Tail. Weixin Zeng, Xiang Zhao, Wei Wang, Jiuyang Tang, andZhen Tan• Regional Relation Modeling for Visual Place Recognition. Yingying Zhu, Biao Li, Jiong Wang, andZhou Zhao
Session 5B (July 28, 15:40-17:40)
Learning for Recommendation
Chair: Nicola Ferro (University of Padua)
Room: Amazon• A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data.
Dugang Liu, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Weike Pan, and Zhong Ming
• Agreement and Disagreement between True and False-Positive Metrics in Recommender Systems
Evaluation. Elisa Mena-Maldonado, Rocío Cañamares, Pablo Castells, Yongli Ren, and Mark
Sanderson• Leveraging Social Media for Medical Text Simplification. Nikhil Pattisapu, Nishant Prabhu, Smriti
Bhati, and Vasudeva Varma• Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning. Wenhui Yu and Zheng Qin• MaHRL: Multi-goals Abstraction based Deep Hierarchical Reinforcement Learning for
Recommendations. Dongyang Zhao, Liang Zhang, Bo Zhang, Lizhou Zheng, Yongjun Bao, andWeipeng Yan• Towards Question-based Recommender Systems. Jie Zou, Yifan Chen, and Evangelos Kanoulas
Session 5C (July 28, 15:40-17:40)
Information Access and Filtering
Chair: Djoerd Hiemstra (Radboud University)
Room: Alibaba• Try This Instead: Personalized and Interpretable Substitute Recommendation. Tong Chen, Hongzhi
Yin, Guanhua Ye, Zi Huang, Yang Wang, and Meng Wang
• Towards Linking Camouflaged Descriptions to Implicit Products in E-commerce. Longtao Huang, Bo
Yuan, Rong Zhang, and Quan Lu• Distributed Equivalent Substitution Training for Large-Scale Recommender Systems. Haidong Rong,Yangzihao Wang, Feihu Zhou, Junjie Zhai, Haiyang Wu, Rui Lan, Fan Li, Han Zhang, Yuekui Yang,
Zhenyu Guo, and Di Wang
• Query Resolution for Conversational Search with Limited Supervision. Nikos Voskarides, Dan Li,
Pengjie Ren, Evangelos Kanoulas, and Maarten de Rijke• Self-Supervised Reinforcement Learning for Recommender Systems. Xin Xin, Alexandros
Karatzoglou, Ioannis Arapakis, and Joemon Jose• Generative Attribute Manipulation Scheme for Flexible Fashion Search. Xin Yang, Xuemeng Song,
Xianjing Han, Haokun Wen, Jie Nie, and Liqiang Nie
Industrial Session IV (July 28, 15:40-17:40)
Chair: Rui Wang (Alibaba Group)
Room: Microsoft• Understanding Echo Chambers in E-commerce Recommender Systems. Yingqiang Ge, Shuya Zhao,
Honglu Zhou, Changhua Pei, Fei Sun, Wenwu Ou, and Yongfeng Zhang

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• Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via
Embedding Learning. Han Zhang, Songlin Wang, Kang Zhang, Zhiling Tang, Yunjiang Jiang, Yun Xiao,
Paul Yan, and Wen-Yun Yang
• GMCM: Graph-based Micro-behavior Conversion Model for Post-click Conversion Rate Estimation.
Wentian Bao, Hong Wen, Sha Li, Xiao-Yang Liu, Quan Lin, and Keping Yang
• A Heterogeneous Information Network based Cross Domain Insurance Recommendation System for
Cold Start Users. Ye Bi, Liqiang Song, Mengqiu Yao, Zhenyu Wu, Jianming Wang, and Jing Xiao
• Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach. Kelong
Mao, Xi Xiao, Jieming Zhu, Biao Lu, Ruiming Tang, and Xiuqiang He
Short/Demo/TOIS Paper Session II (July 28, 20:00-22:00)
Room: Daming Palace
Virtual discussion rooms available
Session 6A (July 28, 22:00-24:00)
Neural Collaborative Filtering 1
Chair: Min Zhang (Tsinghua University)
Room: Meituan• How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models.
Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra, Eugenio Di Sciascio• DPLCF: Differentially Private Local Collaborative Filtering. Chen Gao, Chao Huang, Dongsheng Lin,
Yong Li, and Depeng Jin• Content-aware Neural Hashing for Cold-start Recommendation. Casper Hansen, Christian Hansen,
Jakob Grue Simonsen Stephen Alstrup, and Christina Lioma• Meta Matrix Factorization for Federated Rating Predictions. Yujie Lin, Pengjie Ren, Zhumin Chen,
Zhaochun Ren, Dongxiao Yu, Jun Ma, Maarten de Rijke, and Xiuzhen Cheng• The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation. Tobias
Schnabel, Paul Bennett, Saleema Amershi, Peter Bailey, and Thorsten Joachims• Disentangled Representations for Graph-based Collaborative Filtering. Xiang Wang, Hongye Jin, An
Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua
Session 6B (July 28, 22:00-24:00)
Domain Specific Applications 2
Chair: Krisztian Balog (University of Stavanger)
Room: Amazon• Domain-Adaptive Neural Automated Essay Scoring. Yue Cao, Hanqi Jin, Xiaojun Wan, and Zhiwei Yu• ADORE: Aspect Dependent Online REview Labeling for Review Generation. Parisa Kaghazgaran,
Jianling Wang, Ruihong Huang, and James Caverlee• Finding the Best of Both Worlds: Faster and More Robust Top-k Document Retrieval. Omar Khattab,
Mohammad Hammoud, and Tamer Elsayed• Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste. Zahra Nazari,
Christophe Charbuillet, Johan Pages, Martin Laurent, Denis Charrier, Briana Vecchione, and Benjamin
Carterette• Learning with Weak Supervision for Email Intent Detection. Kai Shu, Subhabrata Mukherjee,
Guoqing Zheng, Ahmed Hassan Awadallah, Milad Shokouhi, and Susan Dumais• 3D Self-Attention for Unsupervised Video Quantization. Jingkuan Song, Ruimin Lang, Xiaosu Zhu,
Xing Xu, Lianli Gao, and Heng Tao Shen

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Session 6C (July 28, 22:00-24:00)
Context-aware Modeling
Chair: Carsten Eickhoff (Brown University)
Room: Alibaba• Modeling Personalized Item Frequency Information for Next-basket Recommendation. Haoji Hu,
Xiangnan He, Jinyang Gao, and Zhi-Li Zhang• Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation. Adit
Krishnan, Mahashweta Das, Mangesh Bendre, Hao Yang, and Hari Sundaram• Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based
Recommendation. Wenjing Meng, Deqing Yang, and Yanghua Xiao
• Next-item Recommendation with Sequential Hypergraphs. Jianling Wang, Kaize Ding, Liangjie Hong,
Huan Liu, and James Caverlee• Encoding History with Context-aware Representation Learning for Personalized Search. Yujia Zhou,
Zhicheng Dou, and Ji-Rong Wen• Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts
Transformation. Ziwei Zhu, Shahin Sefati, Parsa Saadatpanah, and James Caverlee

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Wednesday, July 29, 2020
(Time Zone: UTC+8)
Break (0:00-6:00)
Virtual discussion rooms available
Keynote V (July 29, 8:30-9:30; playback is available after 10:00)
Chair: Yi Chang and Yiqun Liu
Room: Xi’an
Title: From Information to Assistance
Speaker: Elizabeth F. Churchill, Google
Session 7A (July 29, 9:40-11:40)
Conversation and Interactive IR
Chair: Jeff Dalton (University of Glasgow)
Room: Meituan• Neural Representation Learning for Clarification in Conversational Search. Helia Hashemi, Hamed
Zamani, and Bruce Croft• Investigating Reference Dependence Effects on User Search Interaction and Satisfaction. Jiqun Liu
and Fangyuan Han• DukeNet: A Dual Knowledge Interaction Network for Knowledge-Grounded Conversation. Chuan
Meng, Pengjie Ren, Zhumin Chen, Weiwei Sun, Zhaochun Ren, Zhaopeng Tu, and Maarten de Rijke• What If Bots Feel Moods? Towards Controllable Retrieval-based Dialogue Systems with EmotionAware Transition Networks. Lisong Qiu, Ying Wai Shiu, Pingping Lin, Ruihua Song, Yue Liu, Dongyan
Zhao, and Rui Yan• Expressions of Style in Information Seeking Conversation with an Agent. Paul Thomas, Daniel
Mcduff, Mary Czerwinski, and Nick Craswell• Analyzing and Learning from User Interactions for Search Clarification. Hamed Zamani, Bhaskar
Mitra, Everest Chen, Gord Lueck, Fernando Diaz, Paul Bennett, Nick Craswell, and Susan Dumais
Session 7B (July 29, 9:40-11:40)
Text Classification and Transfer Learning
Chair: Rosie Jones (Spotify)
Room: Amazon• A Unified Dual-view Model for Review Summarization and Sentiment Classification with
Inconsistency Loss. Hou Pong Chan, Wang Chen, and Irwin King
• Enhancing Text Classification via Discovering Additional Semantic Clues from Logograms. Chen Qian,
Fuli Feng, Lijie Wen, Li Lin, and Tat-Seng Chua
• Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation. Le Wu,
Yonghui Yang, Lei Chen, Defu Lian, Richang Hong, and Meng Wang
• Web-to-Voice Transfer for Product Recommendation on Voice. Rongting Zhang and Jie Yang
• Minimally Supervised Categorization of Text with Metadata. Yu Zhang, Yu Meng, Jiaxin Huang, FrankF. Xu, Xuan Wang, and Jiawei Han• Joint Aspect-Sentiment Analysis with Minimal User Guidance. Honglei Zhuang, Fang Guo, Chao
Zhang, Liyuan Liu, and Jiawei Han

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Session 7C (July 29, 9:40-11:40)
Neural Collaborative Filtering 2
Chair: Yi Fang (Santa Clara University)
Room: Alibaba• AR-CF: Augmenting Virtual Users and Items in Collaborative Filtering for Addressing Cold-Start
Problems. Dong-Kyu Chae, Jihoo Kim, Sang-Wook Kim, and Duen Horng Chau
• Studying Product Competition Using Representation Learning. Fanglin Chen, Xiao Liu, Davide
Proserpio, Isamar Troncoso, and Feiyu Xiong• Deep Critiquing for VAE-based Recommender Systems. Kai Luo, Hojin Yang, Ga Wu, and Scott
Sanner• GroupIM: A Mutual Information Maximizing Framework for Neural Group Recommendation.
Aravind Sankar, Yanhong Wu, Yuhang Wu, Wei Zhang, Hao Yang, and Hari Sundaram• Neighbor Interaction Aware Graph Convolution Networks for Recommendation. Jianing Sun, Yingxue
Zhang, Wei Guo, Huifeng Guo, Ruiming Tang, Xiuqiang He, Chen Ma, and Mark Coates• A General Network Compression Framework for Sequential Recommender Systems. Yang Sun, Fajie
Yuan, Min Yang, Guoao Wei, Zhou Zhao, and Duo Liu
Industrial Session V (July 29, 9:40-11:40)
Chair: Imed Zitouni (Google)
Room: Microsoft• A Counterfactual Framework for Seller-Side A/B Testing on Marketplaces. Viet Ha-Thuc, Avishek
Dutta, Ren Mao, Matthew Wood, and Yunli Liu• Knowledge Graph-based Event Embedding Framework for Financial Quantitative Investments.
Dawei Cheng, Fangzhou Yang, Xiaoyang Wang, Ying Zhang, and Liqing Zhang• FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval. Dehong Gao,
Linbo Jin, Ben Chen, Minghui Qiu, Yi Wei, Yi Hu, and Hao Wang
• Large Scale Abstractive Multi-Review Summarization (LSARS) via Aspect Alignment. Haojie Pan,
Rongqin Yang, Xin Zhou, Rui Wang, Deng Cai, and Xiaozhong Liu• Be Aware of the Hot Zone: A Warning System of Hazard Area Prediction to Intervene Novel
Coronavirus COVID-19 Outbreak. Zhenxin Fu, Yu Wu, Hailei Zhang, Yichuan Hu, Dongyan Zhao, andRui YanBreak (12:00-14:30)
Virtual discussion rooms available
Keynote VI (July 29, 14:30-15:30; playback is available after 16:00)
Chair: Xueqi Cheng and Yiqun Liu
Room: Xi’an
Title: How Deep Learning Works for Information Retrieval
Speaker: Dacheng Tao, The University of Sydney
Session 8A (July 29, 15:40-17:40)
Domain Specific Retrieval Tasks
Chair: David Carmel (Amazon)
Room: Meituan• Learning Efficient Representations of Mouse Movements toPredict User Attention in Sponsored
Search. Ioannis Arapakis and Luis A. Leiva

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• Query Reformulation in E-Commerce Search. Sharon Hirsch, Ido Guy, Alexander Nus, Arnon Dagan,
and Oren Kurland• Generating Images Instead of Retrieving them: Relevance feedback on Generative Adversarial
Networks. Antti Ukkonen, Pyry Joona, and Tuukka Ruotsalo• Tree-augmented Cross-Modal Encoding for Complex-Query Video Retrieval. Xun Yang, Jianfeng
Dong, Yixin Cao, Xun Wang, Meng Wang, and Tat-Seng Chua
• Nonlinear Robust Discrete Hashing for Cross-Modal Retrieval. Zhan Yang, Jun Long, Lei Zhu, andWenti Huang• Employing Personal Word Embeddings for Personalized Search. Jing Yao, Zhicheng Dou, and Ji-Rong
Wen
Session 8B (July 29, 15:40-17:40)
Multi-modal Retrieval and Ranking
Chair: Benjamin Piwowarski (CNRS)
Room: Amazon• Query Rewriting for Voice Shopping Null Queries. Iftah Gamzu, Marina Haikin, and Nissim Halabi• Joint-modal Distribution-based Similarity Hashing for Large-scale Unsupervised Deep Cross-modal
Retrieval. Song Liu, Shengsheng Qian, Yang Guan, Jiawei Zhan, and Long Ying
• Learning Colour Representations of Search Queries. Paridhi Maheshwari, Manoj Ghuhan A, and
Vishwa Vinay• Web Table Retrieval using Multimodal Deep Learning. Roee Shraga, Haggai Roitman, Guy
Feigenblat, and Mustafa Canim• Online Collective Matrix Factorization Hashing for Large-Scale Cross-Media Retrieval. Di Wang, Quan
Wang, Yaqiang An, Xinbo Gao, and Yumin Tian
• Correlated Features Synthesis and Alignment for Zero-shot Cross-modal Retrieval. Xing Xu, Kaiyi Lin,
Huimin Lu, Lianli Gao, and Heng Tao Shen
Session 8C (July 29, 15:40-17:40)
Sequential Recommendation
Chair: Josiane Mothe (University of Toulouse)
Room: Alibaba• HME: A Hyperbolic Metric Embedding Approach for Next-POI Recommendation. Shanshan Feng,
Lucas Vinh Tran, Gao Cong, Lisi Chen, Jing Li, and Fan Li• Dual Sequential Network for Temporal Sets Prediction. Leilei Sun, Yansong Bai, Bowen Du, Chuanren
Liu, Hui Xiong, and Weifeng Lv• Group-Aware Long- and Short-Term Graph Representation Learning for Sequential Group
Recommendation. Wen Wang, Wei Zhang, Jun Rao, Zhijie Qiu, Bo Zhang, Leyu Lin, and Hongyuan
Zha• Time Matters: Sequential Recommendation with Complex Temporal Information. Wenwen Ye,
Shuaiqiang Wang, Xu Chen, Xuepeng Wang, Zheng Qin, and Dawei Yin• Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation.
Fajie Yuan, Xiangnan He, Alexandros Karatzoglou, and Liguang Zhang• How to Retrain a Recommender System? Yang Zhang, Xiangnan He, Fuli Feng, Chenxu Wang, Meng
Wang, Yan Li, and Yongdong Zhang
Industrial Session VI (July 29, 15:40-17:40)
Chair: Elad Yom-Tov (Microsoft Research)

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43rd International ACM SIGIR Conference on Research and Develop in Information Retrieval

SIGIR 2020

Room: Microsoft• Network on Network for Tabular Data Classification in Real-world Applications, Yuanfei Luo, Hao
Zhou, Weiwei Tu, Yuqiang Chen, Wenyuan Dai, and Qiang Yang
• Identifying Tasks from Mobile App Usage Patterns. Yuan Tian, Ke Zhou, Mounia Lalmas, and Dan
Pelleg• Efficient Image Gallery Representations at Scale through Multi-task Learning. Benjamin Gutelman
and Pavel Levin
Conference Closing and Business Meeting (July 29, 21:00-24:00)
Room: Xi’an

43rd International ACM SIGIR Conference on Research and Develop in Information Retrieval

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SIGIR 2020

SIGIR’20 Short/Demo/TOIS Paper Session I
July 27, 20:00 – 22:00 (Time Zone: UTC+8)
Room: Daming Palace

Short Paper Session I
SP1
SP2
SP3

SP4
SP5
SP6
SP7
SP8
SP9
SP10
SP11
SP12
SP13
SP14
SH15
SP16
SP17
SP18

SP19

SP20

Dynamic Clustering with Discrete Time Event
Karan Aggarwal, Georgios Theocharous, andPrediction
Anup Rao
Leveraging Transitions of Emotions for Sarcasm Ameeta Agrawal, Aijun An, and Manos
Detection
Papagelis
JointMap: Joint Query Intent Understanding for Ali Ahmadvand, Surya Kallumadi, Faizan Javed,
Modeling Intent Hierarchies in E-commerceand Eugene Agichtein
SearchChoppy: Cut Transformers for Ranked List
Dara Bahri, Yi Tay, Che Zheng, Don Metzler, andTruncation
Andrew Tomkins
Studying Ransomware Attacks Using Web Search Chetan Bansal, Pantazis Deligiannis, Chandra
Logs
Sekhar Maddila, and Nikitha Rao
A Transformer-based Embedding Model for
Keping Bi, Qingyao Ai, and W. Bruce Croft
Personalized Product Search
Balancing Reinforcement Learning Training
Limin Chen, Zhiwen Tang, and Grace Hui Yang
Experiences in Interactive Information Retrieval
Metadata Matters in User Engagement
Xiang Chen, Saayan Mitra, and Viswanathan
Prediction
SwaminathanContext-Aware Term Weighting for First-Stage
Zhuyun Dai and Jamie Callan
Passage Retrieval
Summarizing and Exploring Tabular Data in
Zhuyun Dai, Shuo Zhang, Krisztian Balog, andConversational Search
Jamie Callan
Leveraging Adversarial Training in Self-Learning Xin Dong, Yaxin Zhu, Yupeng Zhang, Zuohui Fu,
for Cross-Lingual Text Classification
Dongkuan Xu, Sen Yang, and Gerard de Melo
Response Quality in Human-Chatbot
Jiepu Jiang and Naman Ahuja
Collaborative Systems
High-Precision Extraction of Emerging Concepts Daniel King, Doug Downey, and Daniel S. Weldfrom Scientific Literature
Video Recommendation with Multi-gate Mixture Dingcheng Li, Xu Li, Jun Wang, and Ping Li
of Experts Soft Actor Critic
Auto-annotation for Voice-enabled
Wenyan Li and Ferhan TureEntertainment Systems
Domain Adaptation with Reconstruction for
Xukun Li and Doina Carage
Disaster Tweet Classification
Recipe Retrieval with Visual Query of Ingredients Yen-Chieh Lien, Hamed Zamani, and Bruce Croft
Alleviating the Inconsistency Problem ofZhiwei Liu, Yingtong Dou, Yutong Deng, Hao
Applying Graph Neural Network to Fraud
Peng, and Philip Yu
Detection
Expansion via Prediction of Importance with
Sean MacAvaney, Franco Maria Nardini,
Contextualization
Raffaele Perego, Nicola Tonellotto, Nazli
Goharian, and Ophir Frieder
Reranking for Efficient Transformer-based
Yoshitomo Matsubara, Thuy Vu, and Alessandro

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SP21

SP22
SP23
SP24
SP25
SP26
SP27

Answer Selection
Combining contextualized and noncontextualized query translations to improve
CLIR
Proactive Suggestion Generation: Data andMethods for Stepwise Task AssistanceContextual Re-Ranking with Behavior Aware
Transformers
Towards Explainable Retrieval Models for
Precision Medicine Literature Search
Search Result Explanations Improve Efficiency
and Trust
Query by Example for Cross-Lingual Event
Retrieval
A Test Collection for Relevance and Sensitivity

SP28 A Study of Methods for the Generation of
Domain-Aware Word Embeddings
SP29 Predicting Entity Popularity to Improve Spoken
Entity Recognition by Virtual Assistants
SP30 Data Poisoning Attacks against Differentially
Private Recommender Systems
SP31 Immersive Search: Using Virtual Reality to
Examine How a Third Dimension Impacts the
Searching Process
SP32 Having Your Cake and Eating it Too: Training
Neural Retrieval for Language Inference without
Losing Lexical Match
SP33 Neural Concept Map Generation for Effective
Document Classification with Interpretable
Structured Summarization
SP34 A Case Study of Multi-class Classification with
Diversified Precision Recall Requirements forQuery Disambiguation
SP35 A Study of Neural Matching Models for Crosslingual IR
SP36 Social Media User Geolocation via Hybrid
Attention
SP37 Personalized Query Suggestions

SIGIR 2020

Moschitti
Suraj Nair, Petra Galuščáková, and Douglas
Oard
Elnaz Nouri, Ryen White, Robert Sim, and Adam
Fourney
Chen Qu, Chenyan Xiong, Yizhe Zhang, Corby
Rosset, W. Bruce Croft, and Paul Bennett
Jiaming Qu, Jaime Arguello, and Yue Wang
Jerome Ramos and Carsten Eickhoff
Sheikh Muhammad Sarwar and James Allan
Mahmoud Sayed, William Cox, Jonah Lynn
Rivera, Caitlin Christian-Lamb, Modassir Iqbal,
Douglas Oard, and Katie Shilton
Dominic Seyler and Chengxiang Zhai
Christophe Van Gysel, Manos Tsagkias, Ernest
Pusateri, and Ilya Oparin
Soumya Wadhwa, Saurabh Agrawal, Harsh
Chaudhari, Deepthi Sharma, and Kannan Achan
Austin Ward and Rob Capra

Vikas Yadav, Steven Bethard, and Mihai
Surdeanu
Carl Yang, Jieyu Zhang, Haonan Wang,
Bangzheng Li, and Jiawei Han
Yingrui Yang, Christopher Miller, Peng Jiang,and Azadeh Moghtaderi
Puxuan Yu and James Allan

Cheng Zheng, Jyun-Yu Jiang, Yichao Zhou, Sean
Young, and Wei Wang
Jianling Zhong, Weiwei Guo, Huiji Gao, and BoLong
SP38 Feature Transformation for Neural Ranking
Honglei Zhuang, Xuanhui Wang, Michael
ModelsBendersky, and Marc Najork
SP39 MetaGen: An academic meta-review generation Chaitanya Bhatia, Tribikram Pradhan, andsystemSukomal Pal
SP40 Hier-SPCNet: A Legal Statute Hierarchy-based
Paheli Bhattacharya, Kripabandhu Ghosh,Heterogeneous Network for Computing Legal
Arindam Pal, and Saptarshi Ghosh
Document Similarity

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SIGIR 2020

SP41 DCDIR: A Deep Cross-Domain Recommendation Ye Bi, Liqiang Song, Mengqiu Yao, Zhenyu Wu,
System for Cold Start Users in Insurance Domain Jianming Wang, and Jing Xiao
SP42 A Pairwise Probe for Understanding BERT Fine- Jie Cai, Ping Nie, and Qian Liu
Tuning on Machine Reading Comprehension
SP43 Adversarial Attack and Detection on
Yuanjiang Cao, Xiaocong Chen, Lina Yao, Xianzhi
Reinforcement Learning based Recommendation Wang, and Wei Emma Zhang
System
SP44 Bundle Recommendation with Graph
Jianxin Chang, Chen Gao, Xiangnan He, Yong Li,
Convolutional Networks
and Depeng Jin
SP45 Re-ranking Answer Selection with Similarity
Dong Chen, Shaoliang Peng, Kenli Li, Ying Xu,
Aggregation
and Jinglin Zhang
SP46 JIT^2R: A Joint Framework for Item Tagging and Xu Chen, Changying Du, Xiuqiang He, and Jun
Tag-based Recommendation
Wang
SP47 Attending to Inter-sentential Features in Neural Billy Chiu, Sunil Sahu, Neha Sengupta, Derek
Text Classification
Thomas, and Mohammady Mahdy
SP48 Retrieving Potential Causes from a Query Event Suchana Datta, Debasis Ganguly, Dwaipayan
Roy, Francesca Bonin, Charles Jochim, andMandar Mitra
SP49 Bridging Hierarchical and Sequential Context
Yang Deng, Wenxuan Zhang, Yaliang Li, Min
Modeling for Question-driven Extractive Answer Yang, Wai Lam, and Ying Shen
Summarization
SP50 MGNN: A Multimodal Graph Neural Network for Jianliang Gao, Tengfei Lyu, Fan Xiong, Jianxin
Predicting the Survival of Cancer Patients
Wang, and Zhao Li
SP51 On A Unified Neural Mechanism to Search Over Kunal Goyal, Utkarsh Gupta, Abir De, and
A Corpus of Graphs
Soumen Chakrabarti
SP52 Session-based Recommendation with
Cheng Guo, Mengfei Zhang, Jinyun Fang, Jiaqi
Hierarchical Leaping Networks
Jin, and Mao Pan
SP53 Multi-Branch Convolutional Network for
Wei Guo, Can Zhang, Huifeng Guo, Ruiming
Context-Aware Recommendation
Tang, and Xiuqiang He
SP54 Predicting Session Length for Product Search on Shashank Gupta and Subhadeep Maji
E-commerce Platform
SP55 Dynamic Link Prediction by Integrating Node
Xiaorong Hao, Tao Lian, and Li Wang
Vector Evolution and Local Neighborhood
Representation
SP56 Nonintrusive-Sensing and ReinforcementD Hong, L Miao, and Y Li
Learning Based Adaptive Personalized Music
Recommendation
SP57 Residual-Duet Network with Tree Dependency
Guangyi Hu, Chongyang Shi, Shufeng Hao, andRepresentation for Chinese Question-Answering Yu Bai
Sentiment Analysis
SP58 Neural Mathematical Solver with Enhanced
Zhenya Huang, Qi Liu, Weibo Gao, Jinze Wu, Yu
Formula Structure
Yin, Hao Wang, and Enhong Chen
SP59 Detecting Concept Drift in Medical Triage
Hamish Huggard, Yun Sing Koh, Gillian Dobbie,
and Edmond Zhang
SP60 Evidence Weighted Tree Ensembles for Text
Md Zahidul Islam, Jixue Liu, Jiuyong Li, Lin Liu,Classificationand Wei Kang

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43rd International ACM SIGIR Conference on Research and Develop in Information Retrieval

SIGIR 2020

SP61 Graph Regularization for Multi-lingual Topic
Arnav Kumar Jain, Gundeep Arora, and Rahul
ModelsAgrawal
SP62 Multi-Modal Summary Generation using Multi- Anubhav Jangra, Sriparna Saha, Adam Jatowt,
objective Optimizationand Mohammed Hasanuzzaman
SP63 A Re-visit of Popularity Baseline in
Yitong Ji, Aixin Sun, Jie Zhang, and Chenliang Li
Recommender Systems
SP64 Training Mixed-Objective Pointing Decoders for Harsh Kohli
Block-Level Optimization in Search
Recommendation
SP65 Reinforcement Learning based Recommendation Yu Lei, Hongbin Pei, Hanqi Yan, and Wenjie Li
with Graph Convolutional Q-network
SP66 Crowdsourced Text Sequence Aggregation based Jiyi Li
on Hybrid Reliability and Representation
SP67 MRIF: Multi-resolution Interest Fusion for
Shihao Li, Dekun Yang, and Bufeng Zhang
Recommendation
SP68 Joint Training Capsule Network for Cold Start
Tingting Liang, Congying Xia, Yuyu Yin, andRecommendation
Philip Yu
SP69 Copula Guided Neural Topic Modelling for Short Lihui Lin, Hongyu Jiang, and Yanghui Rao
Texts
SP70 Soft Kernel-based Ranking on a Statistical
Xinshi Lin and Wai Lam
Manifold
SP71 Multi-Level Multimodal Transformer Network for Ao Liu, Shuai Yuan, Chenbin Zhang, Congjian
Multimodal Recipe Comprehension
Luo, Yaqing Liao, Kun Bai, and Zenglin Xu
SP72 Chinese document classification with biBin Liu and Guosheng Yin
directional convolutional language model
SP73 Neural Unified Review Recommendation with
Hongtao Liu, Wenjun Wang, Hongyan Xu, Qiyao
Cross Attention
Peng, and Pengfei Jiao
SP74 Goal-oriented Chatbots with HierarchicalJianfeng Liu, Feiyang Pan, and Ling Luo
Reinforcement Learning
SP75 CapableOf reasoning: A step towards
Jingping Liu, Yanghua Xiao, Ao Wang, Liang He,
commonsense oracle
and Bin Shao
SP76 Interactive Entity Linking Using Entity-Word
Pei-Chi Lo and Ee-Peng Lim
Representations
SP77 Improving Contextual Language Models for
Junyu Lu, Xiancong Ren, Yazhou Ren, Ao Liu, andResponse Retrieval in Multi-Turn Conversation
Zenglin Xu

Demonstration Paper Session I
DE1
DE2
DE3
DE4

A Lightweight Environment for Learning
Zeynep Akkalyoncu Yilmaz, Charles Clarke, andExperimental IR Research Practices
Jimmy Lin
BRENDA: Browser Extension for Fake News
Bjarte Botnevik, Eirik Sakariassen, and Vinay
Detection
Setty
Agent Dialogue: A Platform for Conversational
Adam Czyzewski, Jeffrey Dalton, and Anton
Information Seeking Experimentation
Leuski
DataMirror: Reflecting on One's Data Self (A Tool Amal Htait, Leif Azzopardi, Emma Nicol, andfor Social Media Users to explore their Digital
Wendy Moncur

43rd International ACM SIGIR Conference on Research and Develop in Information Retrieval

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DE5

DE6

DE7
DE8
DE9

Footprints)
Conversational Question Answering overPassages by Leveraging Word Proximity
Networks
FigExplorer: A System for Retrieval andExploration of Figures from Collections ofResearch Articles
APS: An Active PubMed Search System forTechnology Assisted Review
Systematic Review Automation Tools for End-toEnd Query Formulation
Deep Job Understanding at LinkedIn

DE10 Supporting Interoperability Between OpenSource Search Engines with the Common Index
File Format
DE11 Web of Scholars: A Scholar Knowledge Graph

SIGIR 2020

Magdalena Kaiser, Rishiraj Saha Roy, andGerhard Weikum
Saar Kuzi, Chengxiang Zhai, Yin Tian, andHaichuan TangDan Li, Panagiotis Zafeiriadis, and Evangelos
KanoulasHang Li, Harrisen Scells, and Guido Zuccon
Shan Li, Baoxu Shi, Jaewon Yang, Yan Ji, Shuai
Wang, Fei Chen, and Qi He
Jimmy Lin, Joel Mackenzie, Chris Kamphuis,
Craig Macdonald, Antonio Mallia, Michal
Siedlaczek, Andrew Trotman, and Arjen de Vries
Jiaying Liu, Jing Ren, Wenqing Zheng, Lianhua
Chi, Ivan Lee, and Feng Xia

TOIS Paper Session I
TO1
TO2

Binary Sketches for Secondary Filtering
Efficient Learning-Based Recommendation
Algorithms for Top-N Tasks and Top-N Workers
in Large-Scale Crowdsourcing Systems
TO3 A Deep Bayesian Tensor-Based System for Video
Recommendation
TO4 Transfer to Rank for Heterogeneous One-Class
Collaborative Filtering
TO5 Adversarial Distillation for Efficient
Recommendation with External Knowledge
TO6 Personalised Reranking of Paper
Recommendations Using Paper Content andUser Behavior
TO7 Deep Item-based Collaborative Filtering for TopN Recommendation
TO8 Joint Neural Collaborative Filtering for
Recommender Systems
TO9 Enhancing Personalized Recommendation by
Implicit Preference Communities Modeling
TO10 Geographic Diversification of Recommended
POIs in Frequently Visited Areas
TO11 A Multi-Label Classification Method Using a
Hierarchical and Transparent Representation for
Paper-Reviewer Recommendation
TO12 Next-Item Recommendation via Collaborative
Filtering with Bidirectional Item Similarity

Vladimir Mic, David Novak, and Pavel Zezula
Mejdl Safran and Dunren Che

Wei Lu, Fu-Lai Chung, Wenhao Jiang, Martin
Ester, and Wei Liu
Weike Pan, Qiang Yang, Wanling Cai, Yaofeng
Chen, Qing Zhang, Xiaogang Peng, Zhong Ming
Xu Chen, Yongfeng Zhang, Hongteng Xu, Zheng
Qin, and Hongyuan Zha
Xinyi Li, Yifan Chen, Benjamin Pettit, andMaarten de Rijke
Feng Xue, Xiangnan He, Xiang Wang, Jiandong
Xu, Kai Liu, and Richang Hong
Wanyu Chen, Fei Cai, Honghui Chen, andMaarten de Rijke
Xiao Lin, Min Zhang, Yiqun Liu, and Shaoping
Ma
Jungkyu Han and Hayato Yamana
Dong Zhang, Shu Zhao, Zhen Duan, Jie Chen,
Yanping Zhang, and Jie Tang
Zijie Zeng, Jing Lin, Lin Li, Weike Pan, and Zhong
Ming

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TO13 Improving Implicit Recommender Systems with
Auxiliary Data
TO14 Local Variational Feature-Based Similarity
Models for Recommending Top-N New Items
TO15 Efficient Neural Matrix Factorization without
Sampling for Recommendation
TO16 An Enhanced Neural Network Approach to
Person-Job Fit in Talent Recruitment
TO17 From Question to Text: Question-Oriented
Feature Attention for Answer Selection
TO18 Jointly Minimizing the Expected Costs of Review
for Responsiveness and Privilege in E-Discovery
TO19 Fine-grained Geolocation of Tweets in Temporal
Proximity
TO20 Learning from Multi-annotator Data: A Noiseaware Classification Framework
TO21 A Deep Learning Architecture for Psychometric
Natural Language Processing
TO22 Jointly Learning Representations of Nodes andAttributes for Attributed Networks
TO23 Learning Semantic Representations from
Directed Social Links to Tag Microblog Users at
Scale
TO24 OutdoorSent: Sentiment Analysis of Urban
Outdoor Images by Using Semantic and Deep
Features

SIGIR 2020

Jingtao Ding, Guanghui Yu, Yong Li, Xiangnan
He, and Depeng Jin
Yifan Chen, Yang Wang, Xiang Zhao, Hongzhi
Yin, Ilya Markov, Maarten de Rijke
Chong Chen, Min Zhang, Yongfeng Zhang, Yiqun
Liu, Shaoping Ma
Chuan Qin, Hengshu Zhu, Tong Xu, Chen Zhu,
Chao Ma, Enhong Chen, and Hui Xiong
Heyan Huang, Xiaochi Wei, Liqiang Nie, Xianling
Mao, and Xin-Shun Xu
Douglas W. Oard, Fabrizio Sebastiani, and Jyothi
K. Vinjumur
Wen-Haw Chong and Ee-Peng Lim
Xueying Zhan, Yaowei Wang, Yanghui Rao, andQing Li
Faizan Ahmad, Ahmed Abbasi, Jingjing Li, David
G. Dobolyi, Richard G. Netemeyer, Gari D.
Clifford, and Hsinchun Chen
Zaiqiao Meng, Shangsong Liang, Xiangliang
Zhang, Richard McCreadie, and Iadh Ounis
Wayne Xin Zhao, Yupeng Hou, Junhua Chen,
Jonathan J. H. Zhu, Eddy Jing Yin, Hanting Su,
and Ji-Rong Wen
Wyverson Bonasoli de Oliveira, Leyza Baldo
Dorini, Rodrigo Minetto, Thiago H. Silva

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SIGIR 2020

SIGIR’20 Short/Demo/TOIS Paper Session II
July 28, 20:00 – 22:00 (Time Zone: UTC+8)
Room: Daming Palace

Short Paper Session II
SP78
SP79
SP80
SP81
SP82

SP83
SP84
SP85
SP86
SP87
SP88
SP89
SP90

SP91
SP92

SP93
SP94
SP95

SP96

Differentially Private Knowledge Distillation for Lingjuan Lyu and Chi-Hua Chen
Mobile Analytics
Towards Differentially Private Text
Lingjuan Lyu, Yitong Li, Xuanli He, and Tong
Representations
Xiao
Large-scale Image Retrieval with Sparse Binary Changyi Ma, Chonglin Gu, Wenye Li, andProjections
Shuguang Cui
Efficiency Implications of Term Re-Weighting for Joel Mackenzie, Zhuyun Dai, Luke Gallagher,
Passage Retrieval
and Jamie Callan
Read what you need: Controllable Aspect-based Rajdeep Mukherjee, Hari Chandana Peruri,
Opinion Summarization of Tourist Reviews
Uppada Vishnu, Pawan Goyal, Sourangshu
Bhattacharya, and Niloy Ganguly
DC-BERT: Decoupling Question and Document
Ping Nie, Yuyu Zhang, Xiubo Geng,
for Efficient Contextual Encoding
Ramamurthy Arun, Le Song, and Daxin Jiang
An Intent-guided Collaborative Machine for
Zhiqiang Pan, Fei Cai, Yanxiang Ling, andSession-based Recommendation
Maarten de Rijke
Rethinking Item Importance in Session-based
Zhiqiang Pan, Fei Cai, Yanxiang Ling, andRecommendation
Maarten de Rijke
MHM: Multi-modal Clinical Data based
Zhi Qiao, Zhen Zhang, Xian Wu, Shen Ge, andHierarchical Multi-label Diagnosis Prediction
Wei Fan
How Useful are Reviews for Recommendation? Noveen Sachdeva and Julian McAuley
A Critical Review and Potential Improvements
Dual Learning Algorithm for Delayed
Yuta Saito, Gota Morishita, and Shota Yasui
Conversions
Evaluation of Cross Domain Text Summarization Liam Scanlon, Shiwei Zhang, Xiuzhen Jenny
Zhang, and Mark Sanderson
Convolutional Knowledge Tracing: Modeling
Shuanghong Shen, Qi Liu, Enhong Chen, Han
Individualization in Student Learning Process
Wu, Zhenya Huang, Weihao Zhao, Yu Su,
Haiping Ma, and Shijin Wang
G2T: Generating Fluent Descriptions for
Yunzhou Shi, Zhiling Luo, Pengcheng Zhu, Feng
Knowledge GraphJi, Wei Zhou, Haiqing Chen, and Yujiu Yang
Improving Matching Models with Hierarchical
Chongyang Tao, Wei Wu, Yansong Feng,
Contextualized Representations for Multi-turn Dongyan Zhao, and Rui Yan
Response Selection
Visual Intents vs. Clicks, Likes, and Purchases in Riku Togashi and Tetsuya Sakai
E-commerce
MVL: Multi-View Learning for News
Santosh Tokala Yaswanth Sri Sai, Avirup Saha,
Recommendation
and Niloy Ganguly
Query/Task Satisfaction and Grid-based
Kosetsu Tsukuda and Masataka Goto
Evaluation Metrics Under Different Image
Search Intents
Learning Discriminative Joint Embeddings for
Rui Wang, Xin Liu, Yiu-Ming Cheung, Shu-Juan

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SP97
SP98
SP99

SP100
SP101
SP102
SP103
SP104
SP105
SP106
SP107
SP108

SP109
SP110

SP111
SP112

SP113

SP114
SP115
SP116
SP117

SIGIR 2020

Efficient Face and Voice Association
TFNet: Multi-Semantic Feature Interaction for
CTR Prediction
Investigating reading behavior in Fine-grained
Relevance Judgment
Neural Hierarchical Factorization Machines for
User's Event Sequence Analysis

Peng, Nannan Wang, and Wentao Fan
Shu Wu, Feng Yu, Xueli Yu, Qiang Liu, Liang
Wang, Tieniu Tan, Jie Shao, and Fan Huang
Zhijing Wu, Jiaxin Mao, Yiqun Liu, Min Zhang,and Shaoping Ma
Dongbo Xi, Fuzhen Zhuang, Bowen Song,Yongchun Zhu, Shuai Chen, Dan Hong, Tao
Chen, Xi Gu, and Qing He
Label-Consistency based Graph Neural Networks Bingbing Xu, Junjie Huang, Liang Hou, Huawei
for Semi-supervised Node Classification
Shen, Jinhua Gao, and Xueqi Cheng
Symmetric Regularization based BERT for Pair- Weidi Xu, Kunlong Chen, Xingyi Cheng, andwise Semantic Reasoning
Taifeng Wang
Deep Interest with Hierarchical Attention
Weinan Xu, Hengxu He, Minshi Tan, Yunming Li,
Network for Click-Through Rate Prediction
Jun Lang, and Dongbai Guo
A Knowledge-Enhanced Recommendation
Deqing Yang, Zengchun Song, Lvxin Xue, andModel with Attribute-Level Co-Attention
Yanghua Xiao
Multi-source Domain Adaptation for Sentiment Min Yang, Ying Shen, Xiaojun Chen, andClassification with Granger Causal Inference
Chengming Li
TADS: Learning Time-Aware Scheduling Policy
Zhengyu Yang, Jian Shen, Yunfei Liu, Yang Yang,
with Dyna-Style Planning for Spaced Repetition Weinan Zhang, and Yong Yu
TAGNN: Target Attentive Graph NeuralFeng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang
Networks for Session-based Recommendation Wang, and Tieniu Tan
Query Classification with Multi-objective Backoff Hang Yu and Lester Litchfield
Optimization
Influence Function for Unbiased
Jiangxing Yu, Hong Zhu, Chihyao Chang, Xinhua
Recommendation
Feng, Bo-Wen Yuan, Xiuqiang He, and Zhenhua
Dong
Few-Shot Generative Conversational QueryShi Yu, Jiahua Liu, Jingqin Yang, Chenyan Xiong,
Rewriting
Paul Bennett, Jianfeng Gao, and Zhiyuan Liu
User-Inspired Posterior Network for
Haolan Zhan, Hainan Zhang, Hongshen Chen,
Recommendation Reason Generation
Lei Shen, Yanyan Lan, Zhuoye Ding, and Dawei
Yin
An analysis of BERT in document ranking
Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang,and Shaoping Ma
Read, Attend, and Exclude: Multi-Choice
Chebin Zhang, Congjian Luo, Junyu Lu, Ao Liu,
Reading Comprehension by Mimicking Human Bing Bai, and Zenglin Xu
Reasoning Process
Unsupervised Text Summarization with
Jinming Zhao, Ming Liu, Yuan Jin, He Zhao, Lan
Sentence Graph Compression
Du, He Zhang, Longxiang Gao, and Gholamreza
Haffari
Improving Neural Chinese Word Segmentation Xiaoyan Zhao, Min Yang, Qiang Qu, and Yang
with Lexicon-enhanced Adaptive Attention
Sun
Sentiment-guided Sequential Recommendation Lin Zheng, Naicheng Guo, Weihao Chen, Jin Yu,
and Dazhi Jiang
L2R2: Leveraging Ranking for Abductive
Yunchang Zhu, Liang Pang, Yanyan Lan, andReasoning
Xueqi Cheng
Automatic Generation of Topic Labels
Areej Alokaili, Nikolaos Aletras, and Mark
Stevenson

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SP118 Extractive Snippet Generation for Arguments
SP119 Proposal and comparison of health specificfeatures for the automatic assessment of
readability
SP120 MarkedBERT: Integrating Traditional IR cues in
pre-trained language models for passage
retrieval
SP121 Relevance Models for Multi-ContextualAppropriateness in Points-Of-Interest
Recommendation
SP122 CAsT-19: A Dataset for Research on
Conversational Information Seeking
SP123 Predicting Perceptual Speed from Search
Behaviour
SP124 Learning Term Discrimination
SP125 Sampling Bias Due to Near-Duplicates inLearning to Rank
SP126 Enhancing Recommendation Diversity using
Determinantal Point Processes on Knowledge
GraphsSP127 Relevance Transformer: Generating Concise
Code Snippets with Relevance Feedback
SP128 Unsupervised Semantic Hashing with Pairwise
Reconstruction
SP129 Factuality Checking in News Headlines with Eye
Tracking
SP130 ArTest: The First Test Collection for Arabic Web
Search with Relevance Rationales
SP131 Improving Document-Level Text Retrieval using
Local Attention in the Transformer-Kernel
Pooling Model
SP132 Using Exploration to Alleviate Closed-Loop
Effects in Recommender Systems
SP133 A Heterogeneous Graph Neural Model for ColdStart Recommendation
SP134 Query-level Early Exit for Additive Learning-toRank Ensembles

SIGIR 2020

Milad Alshomary, Nick Duesterhus, andHenning Wachsmuth
Hélder Antunes and Carla Teixeira Lopes

Lila Boualili, Jose Moreno, and Boughanem
MohandAnirban Chakraborty, Debasis Ganguly, andOwen Conlan
Jeffrey Dalton, Chenyan Xiong, Vaibhav Kumar,
and Jamie Callan
Olivia Foulds, Alessandro Suglia, Leif Azzopardi,
and Martin Halvey
Jibril Frej, Didier Schwab, Philippe Mulhem, andJean-Pierre Chevallet
Maik Fröbe, Janek Bevendorff, Jan Heinrich
Reimer, Martin Potthast, and Matthias Hagen
Lu Gan, Diana Nurbakova, Léa Laporte, andSylvie Calabretto
Carlos Gemmell, Jeffrey Dalton, and Federico
Rossetto
Casper Hansen, Christian Hansen, Jakob Grue
Simonsen, Stephen Alstrup, and Christina Lioma
Christian Hansen, Casper Hansen, Jakob Grue
Simonsen, Birger Larsen, Stephen Alstrup, andChristina Lioma
Maram Hasanain, Yassmine Barkallah, Reem
Suwaileh, Mucahid Kutlu, and Tamer Elsayed
Sebastian Hofstätter, Hamed Zamani, Bhaskar
Mitra, Nick Craswell, and Allan Hanbury
Amir H. Jadidinejad, Craig Macdonald, and Iadh
Ounis
Siwei Liu, Iadh Ounis, Craig Macdonald, andZaiqiao Meng
Claudio Lucchese, Franco Maria Nardini,
Salvatore Orlando, Raffaele Perego, andSalvatore Trani
Luis Lugo, Jose Moreno, and Gilles Hubert

SP135 Segmenting search query logs by learning todetect search task boundaries
SP136 A multilingual approach for unsupervised search Luis Lugo, Jose Moreno, and Gilles Huberttask identification
SP137 Sequential-based Adversarial Optimisation for
Jarana Manotumruksa and Emine Yilmaz
Personalised Top-N Item Recommendation

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SIGIR 2020

SP138 CrossBERT with Triplet Neural Architecture for
Entity Property Ranking
SP139 Active Learning Stopping Strategies forTechnology-Assisted Sensitivity Review
SP140 Topic Propagation in Conversational Search

SP141
SP142
SP143

SP144

Jarana Manotumruksa, Jeff Dalton, Edgar Meij,and Emine Yilmaz
Graham McDonald, Craig Macdonald, and Iadh
Ounis
Ida Mele, Cristina Ioana Muntean, Franco
Maria Nardini, Raffaele Perego, Nicola
Tonellotto, and Ophir Frieder
Reputation (In)dependence in Ranking Systems: Guilherme Ramos and Ludovico Boratto
Demographics Influence Over Output Disparities
Do Neural Ranking Models Intensify Gender
Navid Rekabsaz and Markus Schedl
Bias?
The Curious Case of IR Explainability: Explaining Procheta Sen, Manisha Verma, Debasis
Document Scores within and across Ranking
Ganguly, and Gareth Jones
ModelsFair Classification with Counterfactual Learning Maryam Tavakol

SP145 Multi-grouping Robust Fair Ranking

Thibaut Thonet and Renders Jean-Michel

SP146 Distilling Knowledge for fast retrieval-based
Amir Vakili Tahami, Kamyar Ghajar, andchat-bots
Azadeh Shakery
SP147 An Analysis of Mixed Initiative and Collaboration Svitlana Vakulenko, Evangelos Kanoulas, andin Information-Seeking Dialogues
Maarten de Rijke
SP148 Cascade Model-based Propensity Estimation for Ali Vardasbi, Maarten de Rijke, and Ilya Markov
Counterfactual Learning to Rank
SP149 Studying Ranking-Incentivized Web Dynamics
Ziv Vasilisky, Moshe Tennenholtz, and Oren
Kurland
SP150 How UMass-FSD Inadvertently Leverages
Dominik Wurzer and Yumeng Qin
Temporal Bias
SP151 On the Reliability of Test Collections to
Emine Yilmaz, Nick Craswell, Bhaskar Mitra,
Evaluating Systems of Different Types
and Daniel Campos
SP152 KLOOS: KL Divergence-based Out-of-Scope
Eyup Halit Yilmaz and Cagri Toraman
Intent Detection in Human-to-Machine
Conversations
SP153 DEXA: Supporting Non-Expert Annotators with Markus Zlabinger, Marta Sabou, Sebastian
Dynamic Examples from Experts
Hofstätter, Mete Sertkan, and Allan Hanbury

Demonstration Paper Session II
DE12 SPot: A tool for identifying operating segments
in financial tables
DE13 Receptor: A Platform for Exploring Latent
Relations in Sensitive Documents
DE14 FactCatch: Incremental Pay-as-You-Go Fact
Checking with Minimal User Effort

Zhiqiang Ma, Steven Pomerville, Mingyang Di,
and Armineh Nourbakhsh
Hitarth Narvala, Graham McDonald, and Iadh
Ounis
Thanh Tam Nguyen, Matthias Weidlich, Hongzhi
Yin, Bolong Zheng, Quang Huy Nguyen, andQuoc Viet Hung Nguyen
DE15 WIRE: An Automated Report Generation System Yunseok Noh, Yongmin Shin, Junmo Park, A-

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using Topical and Temporal Summarization
DE16 Experimaestro and Datamaestro: Experimentand Dataset Managers (for IR)
DE17 QuAChIE: Question Answering based Chinese
Information Extraction System
DE18 Vis-Trec: A System for the In-depth Analysis of
trec_eval Results
DE19 JASSjr: The Minimalistic BM25 Search Engine for
Teaching and Learning Information Retrieval
DE20 ServiceGroup: A Human-Machine Cooperation
Solution for Group Chat Customer Service
DE21 Macaw: An Extensible Conversational
Information Seeking Platform
DE22 REL: An Entity Linker Standing on the Shoulders
of Giants

SIGIR 2020

Yeong Kim, Su Jeong Choi, Hyun-Je Song, SeongBae Park, and Seyoung Park
Benjamin Piwowarski
Dongyu Ru, Zhenghui Wang, Lin Qiu, Hao Zhou,
Lei Li, Weinan Zhang, and Yong Yu
Mahtab Tamannaee, Negar Arabzadeh, andEbrahim Bagheri
Andrew Trotman and Kat Lilly
Minghui Yang, Hengbin Cui, Shaosheng Cao,Yafang Wang, and Xiaolong Li
Hamed Zamani and Nick Craswell
Johannes M. van Hulst, Faegheh Hasibi, Koen
Dercksen, Krisztian Balog, and Arjen P. de Vries

TOIS Paper Session II
TO25 Learning to Adaptively Rank Document Retrieval
System Configurations
TO26 Shallow and Deep Syntactic/Semantic Structures
for Passage Reranking in Question-Answering
Systems
TO27 SMAPH: A Piggyback Approach for Entity-Linking
in Web Queries
TO28 Attentive Long Short-Term Preference Modelingfor Personalized Product Search

Romain Deveaud, Josiane Mothe, Md Zia Ullah,
and Jian-Yun Nie
Kateryna Tymoshenko and Alessandro Moschitti

Marco Cornolti, Paolo Ferragina, Massimiliano
Ciaramita, Stefan Rüd, and Hinrich Schütze
Yangyang Guo, Zhiyong Cheng, Liqiang Nie,
Yinglong Wang, Jun Ma, and Mohan
Kankanhalli
Luca Cagliero, Paolo Garza, and Elena Baralis

TO29 ELSA: A Multilingual Document Summarization
Algorithm Based on Frequent Itemsets and
Latent Semantic Analysis
TO30 Risk-Sensitive Learning to Rank with Evolutionary Daniel Xavier Sousa, Sérgio Canuto, Marcos
Multi-Objective Feature Selection
André Gonçalves, Thierson Couto Rosa, andWellington Santos Martins
TO31 Temporal Relational Ranking for Stock Prediction Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo,
Yiqun Liu, and Tat-Seng Chua
TO32 On Annotation Methodologies for Image Search Yunqiu Shao, Yiqun Liu, Fan Zhang, Min Zhang,
Evaluationand Shaoping Ma
TO33 Using Collection Shards to Study Retrieval
Nicola Ferro, Yubin Kim, and Mark Sanderson
Performance Effect Sizes
TO34 Does Diversity Affect User Satisfaction in Image Zhijing Wu, Ke Zhou, Yiqun Liu, Min Zhang, andSearchShaoping Ma
TO35 The Effects of Working Memory, Perceptual
Jaime Arguello and Bogeum Choi
Speed, and Inhibition in Aggregated Search
TO36 Boosting Search Performance Using QueryRodger Benham, Joel Mackenzie, Alistair
Variations
Moffat, and J. Shane Culpepper

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SIGIR 2020

TO37 Constructing Click Model for Mobile Search with Yukun Zheng, Jiaxin Mao, Yiqun Liu, Cheng Luo,
Viewport TimeMin Zhang, and Shaoping Ma
TO38 Relevance Feedback: The Whole Is Inferior to the Fiana Raiber and Oren Kurland
Sum of Its Parts
TO39 Explainable Product Search with a Dynamic
Qingyao Ai, Yongfeng Zhang, Keping Bi, and W.
Relation Embedding Model
Bruce Croft
TO40 Investigating Searchers’ Mental Models to
Paul Thomas, Bodo Billerbeck, Nick Craswell,
Inform Search Explanations
and Ryen W. White
TO41 Brotli: A General-Purpose Data Compressor
Jyrki Alakuijala, Andrea Farruggia, Paolo
Ferragina, Eugene Kliuchnikov, Robert Obryk,
Zoltan Szabadka, and Lode Vandevenne
TO42 Understanding Faceted Search from Data
Xi Niu, Xiangyu Fan, and Tao Zhang
Science and Human Factor Perspectives
TO43 Handling Massive -Gram Datasets Efficiently
Giulio Ermanno Pibiri and Rossano Venturini
TO44 Funnelling: A New Ensemble Method for
Andrea Esuli, Alejandro Moreo, and Fabrizio
Heterogeneous Transfer Learning and Its
Sebastiani
Application to Cross-Lingual Text Classification
TO45 A Price-per-attention Auction Scheme Using
Ioannis Arapakis, Antonio Penta, Hideo Joho,
Mouse Cursor Informationand Luis A. Leiva
TO46 Learning or Forgetting? A Dynamic Approach for Zhenya Huang, Qi Liu, Yuying Chen, Le Wu, Keli
Tracking the Knowledge Proficiency of Students Xiao, Enhong Chen, Haiping Ma, and Guoping
Hu
TO47 Safe Exploration for Optimizing Contextual
Rolf Jagerman, Ilya Markov, and Maarten deBandits
Rijke

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