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.

强化学习（RL）通过照顾用户的即时和未来奖励，在推荐系统（RS）中取得了理想的性能。然而，现有的基于强化学习的推荐方法假设用户和商品之间只存在单一类型的交互行为（例如点击），而实际的推荐场景涉及多种类型的用户交互行为（例如添加购物车、购买）。在本文中，我们提出了一种用于多行为推荐的多任务强化学习模型（MTRL4Rec），该模型使用单个代理针对用户的不同行为给出不同的操作。具体来说，我们首先引入一个模块化网络，其中模块可以共享或隔离，以捕获用户行为的共性和差异。然后使用任务路由网络为每个行为任务在模块化网络中生成路由。我们采用分层强化学习架构来提高 MTRL4Rec 的效率。最后，为我们的模型训练提出了一种训练算法和进一步改进的训练算法。在两个公共数据集上的实验验证了 MTRL4Rec 的有效性。