Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning
This work addresses scalability and stability issues in conversational recommender systems, which is important for improving user interaction in domains like e-commerce, though it appears incremental as it builds on existing reinforcement learning approaches.
The paper tackles the challenge of integrating conversation and recommendation components in conversational recommender systems by formulating three decision-making problems as a unified policy learning task, resulting in a method that significantly outperforms state-of-the-art methods on benchmark datasets and a real-world application.
Conversational recommender systems (CRS) enable the traditional recommender systems to explicitly acquire user preferences towards items and attributes through interactive conversations. Reinforcement learning (RL) is widely adopted to learn conversational recommendation policies to decide what attributes to ask, which items to recommend, and when to ask or recommend, at each conversation turn. However, existing methods mainly target at solving one or two of these three decision-making problems in CRS with separated conversation and recommendation components, which restrict the scalability and generality of CRS and fall short of preserving a stable training procedure. In the light of these challenges, we propose to formulate these three decision-making problems in CRS as a unified policy learning task. In order to systematically integrate conversation and recommendation components, we develop a dynamic weighted graph based RL method to learn a policy to select the action at each conversation turn, either asking an attribute or recommending items. Further, to deal with the sample efficiency issue, we propose two action selection strategies for reducing the candidate action space according to the preference and entropy information. Experimental results on two benchmark CRS datasets and a real-world E-Commerce application show that the proposed method not only significantly outperforms state-of-the-art methods but also enhances the scalability and stability of CRS.