Talk Info: Xiangyu Zhao

Deep Reinforcement Learning for Recommender Systems

Abstract: Recommender systems have become increasingly important in our daily lives since they play an important role in mitigating the information overload problem, especially in many user-oriented online services. Most recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed greedy strategy, which may fail given the dynamic nature of the users’ preferences. Also, they are designed to maximize the immediate reward of recommendations, while completely overlooking their long-term influence on user experiments. To learn adaptive recommendation policy, we will consider the recommendation procedure as sequential interactions between users and recommender agents; and leverage Reinforcement Learning (RL) to automatically learn an optimal recommendation strategy (policy) that maximizes cumulative rewards from users without any specific instructions. Recommender systems based on reinforcement learning have two advantages. First, they can continuously update their strategies during the interactions. Second, the optimal strategy is made by maximizing the expected long-term cumulative reward from users. This talk will introduce the fundamentals and advances of deep reinforcement learning and its applications in recommender systems.

Bio:

Dr. Xiangyu Zhao is an assistant professor of the school of data science at City University of Hong Kong (CityU). His current research interests include data mining and machine learning, especially (1) Information Retrieval and its applications in Personalization, Recommender System, Online Advertising and Search Engine; (2) Urban Computing and Spatio-Temporal Data Analysis; and (3) Reinforcement Learning, AutoML, and Multimodal ML. He has published more than 20 papers in top conferences (e.g., KDD, WWW, AAAI, SIGIR, ICDE, CIKM, ICDM, WSDM, RecSys) and journals (e.g., SIGKDD, SIGWeb, EPL, APS). His research received ICDM’21 Best-ranked Papers, Global Top 100 Chinese New Stars in Artificial Intelligence, CCF-Tencent Open Fund, Criteo Research Award, Bytedance Research Award and MSU Dissertation Fellowship. He serves as top data science conference (senior) program committee members and session chairs (e.g., KDD, AAAI, IJCAI, ICML, ICLR, CIKM), and journal reviewers (e.g., TKDE, TKDD, TOIS, CSUR). He serves as the organizers of DRL4KDD@KDD’19, DRL4IR@SIGIR’20, 2nd DRL4KD@WWW’21, 2nd DRL4IR@SIGIR’21, a lead tutor at WWW’21 and IJCAI’21, and one of the founding academic committee members of MLNLP, the largest AI community in China with 800,000 members/followers. The models and algorithms from his research have been launched in the online system of many companies.

Video Recording and Slides