User Behavior Modeling with Deep Learning for Recommendation: Recent Advances (RecSys 23)
User Behavior Modeling (UBM) plays a critical role in user interest learning, and has been extensively used in recommender systems. The exploration of key interactive patterns between users and items has yielded significant improvements and great commercial success across a variety of recommendation tasks. This tutorial aims to offer an in-depth exploration of this evolving research topic. We start by reviewing the research background of UBM, paving the way to a clearer understanding of the opportunities and challenges. Then, we present a systematic categorization of existing UBM research works, which can be categorized into four different directions including Conventional UBM, Long-Sequence UBM, Multi-Type UBM, and UBM with Side Information. To provide an expansive understanding, we delve into each category, discussing representative models while highlighting their respective strengths and weaknesses. Furthermore, we elucidate on the industrial applications of UBM methods, aiming to provide insights into the practical value of existing UBM solutions. Finally, we identify some open challenges and future prospects in UBM. This comprehensive tutorial serves to provide a solid foundation for anyone looking to understand and implement UBM in their research or business.
- Our survey paper: A Survey on User Behavior Modeling in Recommender Systems
- Our slides: User Behavior Modeling with Deep Learning for Recommendation: Recent Advances
Outline of the tutorial
- Introduction (10min)
- Conventional UBM (5min)
2.1 Network structures: RNN, CNN, Attention
- Long-Sequence UBM (15min)
3.1 Memory-augmented methods
3.2 User behavior retrieval methods
- Multi-Type UBM (15min)
4.1 Late fusion methods
4.2 Early fusion methods
- UBM with Side Information (15min)
5.1 Time information
5.2 Item attribute
5.3 Multi-modal information
- Industrial practices (10min)
- New trends and techniques (10min)
7.1 Reinforcement learning
7.2 Large language models
Weiwen Liu. Researcher, Huawei Noah’s Ark Lab. She received her Ph.D. in Computer Science and Engineering from the Chinese University of Hong Kong in 2020. Her research is broadly concerned with ranking/re-ranking, recommender systems, information retrieval, and user preference learning.
Yong Liu. Senior Principal Researcher, Huawei Noah’s Ark Lab. Prior to joining Huawei, he was a Senior Research Scientist at Nanyang Technological University (NTU), a Data Scientist at NTUC Enterprise, and a Research Scientist at Institute for Infocomm Research (I2R), A*STAR, Singapore. He received his Ph.D. degree in Computer Engineering from NTU in 2016 and B.S. degree in Electronic Science and Technology from University of Science and Technology of China (USTC) in 2008. His current research interests include Large Language Models, Search and Recommendation Systems. He has been invited as a Area Chair/(Senior) PC member of major conferences such as ICLR, NeurIPS, KDD, WWW, SIGIR, ACL, IJCAI, AAAI, and reviewer for IEEE/ACM transactions.
Wei Guo. Researcher, Huawei Noah’s Ark Lab. He got his MS from Wuhan University in 2019. His research interests include recommender systems, deep learning, and graph neural networks.
Hao Wang. Associate Researcher, University of Science and Technology of China. He is also a member of Anhui Province Key Laboratory of Big Data Analysis and Application (BDAA) led by Prof. Enhong Chen and Key Laboratory of Cognitive Intelligence. His research interests include machine learning, data mining, and deep learning, expecially focus on following topics: representation learning, graph mining, sequential modeling, large language model & in-context learning, and also other relevant applications in data mining recently.
Kefan Wang. Master Student, University of Science and Technology of China. His research interests include recommendataion system, deep learning, graph learning and large language model.