Modeling User Fatigue for Sequential Recommendation - Session M3.6

Explore a 12-minute conference talk from the Association for Computing Machinery (ACM) that delves into modeling user fatigue for sequential recommendation systems. Learn about the innovative research conducted by authors Nian Li, Xin Ban, Cheng Ling, Chen Gao, Lantao Hu, Peng Jiang, Kun Gai, Yong Li, and Qingmin Liao. Gain insights into how user fatigue affects recommendation performance and discover potential strategies to mitigate its impact. Understand the importance of considering user behavior patterns in designing more effective and user-friendly recommendation algorithms. This presentation, part of the Users and Simulations session at SIGIR 2024, offers valuable knowledge for researchers and practitioners in the field of information retrieval and recommender systems.

SIGIR 2024 M3.6 [fp] Modeling User Fatigue for Sequential Recommendation