User Multi-preferences Fusion for Conversational Recommender Systems

Conversational recommender systems (CRS) aim to provide recommendations by inferring user preferences during conversations. Many current CRS models utilize third-party information, such as reviews, to supplement the extraction of user preferences. Consequently, users develop preferences for third-party information and their own preferences extracted from original dialog data. However, the prevailing approach of combining these preferences as a unified whole for self-attention at the element level compromises their independence. In real-life decision-making, we refer to third-party information and it is important to distinguish whether the reference is from a third party or from the original dialog data. This paper emphasizes the independence of users’ own preferences and third-party information. To effectively integrate multiple user preferences, we propose an Attentive Wide Deep Conversational Recommender (AWDCore). Specifically, we design an attentive wide linear module and an attentive deep neural network to capture the low-order linear and high-order nonlinear relationships between the user’s own preference and third-party information, respectively. To highlight the significance of the user’s current preference, we incorporate attention mechanisms and a SENet layer in the wide module and deep neural networks, respectively. The learned user preferences are then employed for recommendation and dialogue generation. Extensive experiments have demonstrated the effectiveness of our approach in both recommendation and conversation tasks.