Adaptive and Automated Deep Recommender Systems

Speaker:
Prof. ZHAO Xiangyu
Assistant Professor, School of Data Science
City University of Hong Kong (CityU)

Abstract:

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.

Biography:

Prof. 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, and their applications in Recommender System, Smart City, Healthcare, Carbon Neutral and Finance. He has published more than 60 papers in top conferences (e.g., KDD, WWW, AAAI, SIGIR, IJCAI, ICDE, CIKM, ICDM, WSDM, RecSys, ICLR) and journals (e.g., TOIS, SIGKDD, SIGWeb, EPL, APS). His research has been awarded ICDM’22 and ICDM’21 Best-ranked Papers, Global Top 100 Chinese New Stars in AI, CCF-Ant Research Fund, CCF-Tencent Open Fund, Criteo Faculty Research Award, Bytedance Research Collaboration Award, and nomination for Joint AAAI/ACM SIGAI Doctoral Dissertation Award. He serves as top data science conference (senior) program committee members and session chairs, and journal guest editors and reviewers. He serves as the organizers of DRL4KDD@KDD’19/WWW’21 and DRL4IR@SIGIR’20/21/22 and a lead tutor at WWW’21/22/23, IJCAI’21 and WSDM’23. He also serves as the founding academic committee members of MLNLP, the largest Chinese AI community with millions of members/followers. 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/.

Join Zoom Meeting:
https://cuhk.zoom.us/j/96382199967
Meeting ID: 963 8219 9967

Enquiries: Mr Jeff Liu at Tel. 3943 0624