Click is not equal to purchase: multi-task reinforcement learning for multi-behavior recommendation

Reinforcement learning (RL) has achieved ideal performance in recommendation systems (RSs) by taking care of both immediate and future rewards from users. However, the existing RL-based recommendation methods assume that only a single type of interaction behavior (e.g., clicking) exists between user and item, whereas practical recommendation scenarios involve multiple types of user interaction behaviors (e.g., adding to cart, purchasing). In this paper, we propose a Multi-Task Reinforcement Learning model for multi-behavior Recommendation (MTRL4Rec), which gives different actions for users' different behaviors with a single agent. Specifically, we first introduce a modular network in which modules can be shared or isolated to capture the commonalities and differences across users' behaviors. Then a task routing network is used to generate routes in the modular network for each behavior task. We adopt a hierarchical reinforcement learning architecture to improve the efficiency of MTRL4Rec. Finally, a training algorithm and a further improved training algorithm are proposed for our model training. Experiments on two public datasets validated the effectiveness of MTRL4Rec.