An Off-Policy Reinforcement Learning Algorithm Customized for Multi-Task Fusion in Large-Scale Recommender Systems

arXiv:2404.17589v1 Announce Type: cross
Abstract: Recommender Systems (RSs) are widely used to provide personalized recommendation service. As the last critical stage of RSs, Multi-Task Fusion (MTF) is responsible for combining multiple scores outputted by Multi-Task Learning (MTL) into a final score to maximize user satisfaction, which determines the ultimate recommendation results. Recently, to optimize long-term user satisfaction within a recommendation session, Reinforcement Learning (RL) is used for MTF in the industry. However, the off-policy RL algorithms used for MTF so …

abstract algorithm arxiv cs.ir cs.lg fusion multiple multi-task learning personalized policy recommendation recommender systems reinforcement reinforcement learning responsible rss scale service stage systems type