EnhancedRL: An Enhanced-State Reinforcement Learning Algorithm for Multi-Task Fusion in Recommender Systems

Understand

As a key stage of Recommender Systems (RSs), Multi-Task Fusion (MTF) is responsible for merging multiple scores output by Multi-Task Learning (MTL) into a single score, finally determining the recommendation results.

• Recently, Reinforcement Learning (RL) has been applied to MTF to maximize long-term user satisfaction within a recommendation session.
• However, due to limitations in modeling paradigm, all existing RL algorithms for MTF can only utilize user features and statistical features as the state to generate actions at the user level, but unable to leverage item features and other valuable features, which leads to suboptimal performance.
• Overcoming this problem requires a breakthrough in the existing modeling paradigm, yet, to date, no prior work has addressed it.