WSDM 2023 Workshop on Interactive Recommender Systems
Will be held in 2023

Summary

Recommender systems have been widely applied in different scenarios of our daily lives. Existing recommender systems mainly focus on predicting the user’s future behaviors (e.g., rating, clicking, watching, and purchasing) items but ignore the interactions between the user and the recommender agent. Therefore, they usually generate un-satisfactory recommendation results and cause bad user experience. This motivates the development of interactive recommender systems (IRS) that encourage the interactions between the user and the recommender agent. In recent years, interactive recommender systems have attracted increasingly research attentions from both academia and industry. This workshop provides a forum for researchers and practitioners to share the new techniques and applications of interactive recommender systems, and discuss the future research directions.

Invited Speakers

Aixin Sun

Aixin Sun

Associate Professor
Nanyang Technological University

On Challenges in Evaluating Interactive Recommender Systems

Abstract
Recommender System (RecSys) has been heavily researched for more than 20 years. There are not short of RecSys models in research publications, or practical systems that support our daily needs. Although various experiments have been reported in research papers, the evaluation of RecSys has not been a key focus. In this talk, I will review the challenges in evaluating RecSys and then discuss the possible challenges in evaluating interactive RecSys.

Bio
Dr. Aixin Sun is an Associate Professor at the School of Computer Science and Engineering (SCSE), NTU Singapore. He received Ph.D. in Computer Engineering from NTU in 2004. Dr. Sun is an associate editor of ACM TOIS, Neurocomputing, an editorial board member of JASIST and the Information Retrieval Journal. He was a member of the best short paper committee for both SIGIR2020 and SIGIR2022. He has also served as Area Chair, Senior PC member or PC member for many conferences including SIGIR, WWW, WSDM, EMNLP, AAAI, and IJCAI.

X. Jessie Yang

X. Jessie Yang

Associate Professor
University of Michigan

Combining empirical and computational approaches to model and predict trust dynamics in human-AI interaction

Abstract
Trust in AI/autonomy has been identified as one central factor in effective human-AI interaction. Despite active research in the past 30 years, most studies have used a “snapshot” view of trust and evaluated trust using questionnaires administered at the end of an experiment. This “snapshot” view does not fully acknowledge that trust is a dynamic variable that can strengthen and decay over time. With few exceptions, we have little understanding of the temporal dynamics of trust formation and evolution nor of how trust changes over time due to moment-to-moment interactions with autonomy. In this talk, I will present the results of two studies examining trust dynamics in human-autonomy interaction. In study 1, we identify and define three properties of trust dynamics, namely continuity, negativity bias, and stabilization. The three properties characterize a human agent’s trust formation and evolution process de facto. In study 2, we propose a computational model of trust dynamics that adheres to the three properties and evaluate the computational model against existing trust inference models. Results show that our model provides superior prediction performance and guarantees good model explainability and generalizability.

Bio
Dr. X. Jessie Yang is a Richard Wilson faculty scholar and associate professor in the Department of Industrial and Operations Engineering, University of Michigan, with a courtesy appointment at the School of Information. She received her PhD in Mechanical and Aerospace Engineering (Human Factors) from Nanyang Technological University, Singapore, and was a postdoctoral fellow in the Computer Science and Artificial Intelligence Lab at MIT prior to joining U-M. Her research interests include human-autonomy/robot interaction and human factors in high-risk industries. Her work is supported by grants from the National Science Foundation, National Institute of Health, Department of Defense, including an NSF CAREER award, as well as from various industrial collaborations, including Boeing and GM.

Lizi Liao

Lizi Liao

Assistant Professor
Singapore Management University

On the Proactiveness in Conversational Search and Recommendation

Abstract
Search and recommendation systems suffer from the basic information asymmetry problem between the user and system. Conversation offers a natural and convenient way to bridge the two. Although various findings or models have been reported in research papers and deployed in real systems, the proactiveness of such conversation systems has been less investigated. In this talk, I will first introduce the proactive conversation paradigm and then discuss methods to equip conversational search and recommenders with the ability to interact with end users in a more proactive way.

Bio
Dr. Lizi Liao is an Assistant Professor of Computer Science at Singapore Management University. She received Ph.D. from National University of Singapore in 2019. Dr Liao’s research interests center on task-oriented dialogues, proactive conversational agents, and multimodal conversational search and recommendation as the application target. One of her work was nominated in the ACM MM Best Paper Final List in 2018. She serves as Senior PC member or PC member of prestigious conferences. She also serves as organizing committee members of ACM MM 2019, NLPCC 2022 and WSDM 2023, while chairs sessions at KDD and SIGIR.

Call for Papers

This workshop welcomes submissions from both academia and industry researchers relevant to interactive recommender systems. Topics of interests include but are not limited to:

    • Bandit learning for interactive recommender systems
    • Deep reinforcement learning for interactive recommender systems
    • Conversational recommender systems
    • Multi-modal conversational recommender systems
    • Critiquing-based recommender systems
    • Human decision making in interactive recommender systems
    • Human-recommender interaction in recommender systems
    • Fairness in interactive recommender systems
    • Diversified interactive recommender systems
    • Explanatory interactive recommender systems
    • Knowledge-enhanced interactive recommender systems
    • Personalized interaction mechanism design
    • User simulation for interactive recommender systems
    • Methodology for evaluating interactive recommender systems

Submissions must be in PDF, up to 8 pages long (plus unlimited pages for references), and shorter papers are also welcome. All manuscripts should be formatted according to the new ACM format published in ACM guidelines, selecting the generic “sigconf” sample. Papers can be submitted through easychair.

All submitted papers will be peer reviewed by the program committee. Accepted submissions will be presented at the workshop and published on the workshop’s website, but will not be archived.

Important Dates

  • Paper Submission Deadline: 15 Jan 2023 (11:59 PM, AoE)
  • Acceptance Notification: 29 Jan 2023 (11:59 PM, AoE)
  • Workshop Date: 3 March 2023

Workshop Organizers

Dr. Yong Liu

Senior Principal Researcher

Huawei Noah’s Ark Lab, Singapore

 

Dr. Hao Zhang

Principal Engineer

Huawei Noah’s Ark Lab, Singapore

 

Dr. Zhu Sun

Research Scientist

Institute of High Performance Computing, A*STAR, Singapore

 

Dr. Shoujin Wang

Lecturer

University of Technology Sydney, Australia

 

Dr. Jie Zhang

Professor

Nanyang Technological University, Singapore

 

Dr. Rui Zhang

Visiting Professor

Tsinghua University, China

 

Contact