Explore a 13-minute conference presentation from SIGIR 2024 that delves into enhancing long-term recommender system performance through user-oriented exploration policies. Learn how authors Changshuo Zhang, Sirui Chen, Xiao Zhang, Sunhao Dai, Weijie Yu and Jun Xu address the challenges of session-based recommendations and long-term user engagement. Discover innovative approaches to reinforcement learning in recommendation systems, focusing on exploration strategies that adapt to user preferences over time. Gain insights into methods for balancing immediate user satisfaction with sustained system performance in the context of session-based recommendation scenarios.
Reinforcing Long-Term Performance in Recommender Systems with User-Oriented Exploration Policy
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 W1.2 [fp] Reinforcing Long-Term Performance in RecSys User-Oriented Exploration Policy
Taught by
Association for Computing Machinery (ACM)
Related Courses
-
Treatment Effect Estimation for User Interest Exploration on Recommender Systems
-
Large Language Models as Learnable Planners for Long-Term Recommendation
-
UniSAR: Modeling User Transition Behaviors between Search and Recommendation - Session M3.6
-
Disentangling ID and Modality Effects for Session-based Recommendation
-
Modeling User Fatigue for Sequential Recommendation - Session M3.6
-
An Empirical Analysis on Multi-turn Conversational Recommender Systems - Lecture 3.2
