A survey on reinforcement learning for recommender systems

• Main Authors: Lin, Yuanguo, Liu, Yong, Lin, Fan, Zou, Lixin, Wu, Pengcheng, Zeng, Wenhua, Chen, Huanhuan, Miao, Chunyan
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering; Interactive Recommendation; Policy Gradient
• Online Access: https://hdl.handle.net/10356/170573
• Institution: Nanyang Technological University

Recommender systems have been widely applied in different real-life scenarios to help us find useful information. In particular, reinforcement learning (RL)-based recommender systems have become an emerging research topic in recent years, owing to the interactive nature and autonomous learning abili...