报告时间：2025年07月02日（星期三）9:00-11:30
报告地点：合肥工业大学管理学院1425会议室
报告人：李哲鹏 副教授
工作单位：香港大学
举办单位：合肥工业大学管理学院
报告简介：
The remarkable success in personalized recommendations on digital platforms has sparked interest in extending this advancement to physical spaces. In response, our study introduces a generalized recommendation problem, named Pointof-interest (POI) recommendations in Physical spaces with Pedestrian Movement. (P3M). A critical yet under-investigated impediment in addressing P3M is exposure bias: when the exposure likelihood of items to users is unevenly distributed, indiscriminately treating all unobserved user–item interactions as negative feedback introduces bias to the learning of recommender systems. Unlike existing debiasing literature on digital platforms, we focus on the unique source of uneven exposure in physical spaces—arising from the dynamic interaction between pedestrian movement and spatial layout. To address this issue, we propose a novel recommendation method, Unbiased Movement-aware Pairwise Ranking (UMPR), which considers dynamic. pedestrian movement to achieve unbiased POI recommendations. Specifically, we formulate an unbiased pairwise learning framework, propose a movement-aware recommendation model, and devise an alternating learning algorithm to optimize model parameters. Using real-world mall data, we demonstrate that our method outperforms state-of-the-art benchmarks in delivering store recommendations for pedestrian shoppers. Further investigations confirm that the improved recommendation performance translates into added monetary value, while maintaining humanistic fairness across customers and store tenants. Overall, this study underscores the significance of addressing exposure bias through adequate spatial movement modeling, paving the way for effective recommendations in the physical landscape.
报告人简介：
Dr. Zhepeng (Lionel) Li is an associate professor of Information Systems in the IIM area at the Faculty of Business and Economics, The University of Hong Kong. He received PhD degree in Operations and Information Systems from University of Utah, USA. His research focuses on computational design science, with a particular emphasis on leveraging machine learning and artificial intelligence technologies to address business and societal challenges. His work has been published in leading journals across disciplines, including Management Science, Information Systems Research, MIS Quarterly, and ACM Transactions. Dr. Li’s studies have been supported by various sources, including the General Research Fund from the University Grants Committee of Hong Kong (GRF), Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (NSERC), General Program from the National Natural Science Foundation of China (NSFC), and industrial sponsors. He has received design science research award at INFORMS and participated in serving international associations and organizing events, such as INFORMS College on AI, INFORMS AI cluster, INFORMS workshop on Data Science, and Summer Workshop on AI for Business.
