报告题目：Adaptive and Automated Deep Recommender Systems

报 告 人：赵翔宇 助理教授 香港城市大学

报告时间：2025年1月8日（星期三）15:00-16:30

报告地点：扬子津校区信息学院电工中心N104，线上（腾讯会议218-640-756）

主办单位：信息工程学院（人工智能学院）、江苏省知识管理与智能服务工程研究中心、科学技术处

欢迎广大师生参加!

报告摘要：

Deep recommender systems have become increasingly popular in recent years, and have been utilized in a variety of domains, including movies, music, books, search queries, and social networks. They assist users in their information-seeking tasks by suggesting items (products, services, or information) that best fit their needs and preferences. Most existing recommender systems are based on static recommendation policies and hand-crafted architectures. Specifically, (i) most recommender systems consider the recommendation procedure as a static process, which may fail given the dynamic nature of the users' preferences; (ii) existing recommendation policies aim to maximize the immediate reward from users, while completely overlooking their long-term impacts on user experience; (iii) designing architectures manually requires ample expert knowledge, non-trivial time and engineering efforts, while sometimes human error and bias can lead to suboptimal architectures. I will introduce my efforts in tackling these challenges via reinforcement learning (RL) and automated machine learning (AutoML), which can (i) adaptively update the recommendation policies, (ii) optimize the long-term user experience, and (iii) automatically design the deep architectures for recommender systems.

报告人简介：

Prof. Xiangyu Zhao is an assistant professor of data science at City University of Hong Kong (CityU). His current research interests include data mining and machine learning, especially (1) Personalization, Recommender System, Online Advertising, Search Engine, and Information Retrieval; and (2) Large Language Models, Deep Reinforcement Learning, AutoML, Trustworthy AI and Multimodal ML. He has published more than 140 papers in top conferences and journals. His research has been awarded ICDM’22 and ICDM’21 Best-ranked Papers, 2021 Joint AAAI/ACM SIGAI Doctoral Dissertation Award Nomination, Hong Kong RGC Research Impact Fund, CCF-Tencent Open Fund (twice), CCF-Alibaba Research Fund, Huawei Innovation Research Program, Tencent Focused Research Fund. He is among the top 2% in the Stanford list of the world’s mostcited scientists in 2024. He serves as top data science and AI conference organizers (e.g., WWW’25, APWeb-WAIM’24), area chairs (e.g., KDD, ICML, ICLR, IJCAI, AISTATS), and journal editors (e.g., Neural Networks). The models and algorithms from his research have been launched in the online system of many companies. Please find more information at https://zhaoxyai.github.io/.