Weiwen Liu

Weiwen Liu

Tenure-track Associate Professor

Talks and Tutorials

User Behavior Modeling with Deep Learning for Recommendation: Recent Advances

September 18, 2023

Tutorial, RecSys 2023, Singapore

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. More information here

How Recommender Systems Can Benefit from Large Language Models: An Application Perspective

January 01, 2023

Talk, DataFun, SusTech, NTU, Juejin,

Recommender systems (RS) play a crucial role in mitigating information overload and delivering personalized content that caters to users’ diverse needs. The emergence of large language models (LLMs) offers a new horizon for redefining recommender systems with vast general knowledge and reasoning capabilities. In this talk, we trace the technical evolution of recommender systems, highlighting key advancements from deep learning to LLMs and LLM agents.

Neural Re-ranking for Multi-stage Recommender Systems

September 18, 2022

Tutorial, RecSys 2022, Seattle, Washington, USA|Online

Re-ranking is one of the most critical stages for multi-stage recommender systems (MRS), which re-orders the input ranking lists by modeling the cross-item interaction. Recent re-ranking methods have evolved into deep neural architectures due to the significant advances in deep learning. Neural re-ranking, therefore, has become a trending topic and many of the improved algorithms have demonstrated their use in industrial applications, enjoying great commercial success. The purpose of this tutorial is to explore some of the recent work on neural re-ranking, integrating them into a broader picture and paving ways for more comprehensive solutions for future research. In particular, we provide a taxonomy of current methods according to the objectives and training signals. We examine and compare these methods qualitatively and quantitatively, and identify some open challenges and future prospects.More information here