TIME
2024年11月22日(周五)10:00-11:30
VENUE
信管学院102报告厅
HOST
上海财经大学信息管理与工程学院、人工智能与金融大模型双一流特区团队
SPEAKER
Prof. Li Chen(陈黎) is currently a Professor and Associate Head (Research) in the Department of Computer Science at Hong Kong Baptist University (HKBU). She obtained her PhD degree in Computer Science from the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, and Bachelor and Master degrees from Peking University, China. Her recent research focus has mainly been on online decision support, data-driven personalization, and recommender systems, with applications covering various domains including entertainment, digital media, education, e-commerce, and psychological well-being. She has authored and co-authored over 150 publications, with 10,400 citations so far (H-index 49). Her co-authored papers have received several awards, including the RecSys’24 Best Student Paper Award, CHI’22 Honourable Mention Award, UMAP’20 Best Student Paper Award, UMUAI 2018 Best Paper Award, and UMAP’15 Best Student Paper Award. She received the President’s Award for Outstanding Performance in Research Supervision 2022/23, and has been included in the list of the world’s top 2% most-cited scientists by Stanford University since 2021. She is the editor-in-chief of ACM Transactions on Recommender Systems (TORS), executive committee member of ACM Conference on Recommender Systems (RecSys), editorial board member of User Modeling and User-Adapted Interaction Journal (UMUAI), and associate editor of ACM Transactions on Interactive Intelligent Systems (TiiS). She also served as the general co-chair of ACM RecSys’23, the program co-chair of ACM RecSys’20, and the program co-chair of ACM UMAP’18.
TITLE
A User-Centric Approach to Developing Trustworthy Recommender Systems
ABSTRACT
Recommender systems have become increasingly prevalent across various online platforms (e.g., e-commerce, entertainment, and social media), delivering personalized information and services tailored to individual users. While numerous studies have concentrated on enhancing recommendation accuracy to engage users, there is a pressing need for a more user-centric approach that prioritizes practical benefits for users. In this talk, I will present a user-centric methodology for effectively utilizing recommender systems. Specifically, I will discuss two case studies. The first case study highlights the integration of tradeoff decision support within recommender systems to improve users’ decision-making quality in high-stakes product domains. The second case study addresses the filter bubble phenomenon by considering users’ personal characteristics, such as personality traits, to develop personalized diversity strategies. Both studies demonstrate the significant impact of these design factors on enhancing users’ trust in the system. The findings have the potential to advance research on trustworthy and responsible recommender systems from the users’ perspective.
