Topic-Guided Relational Conversational Recommender in Multi-Domain

Conversational systems have recently attracted significant attention. Both the research community and industry believe that it will exert huge impact on human-computer interaction, and specifically, the IR/RecSys community has begun to explore Conversational Recommendation. In real-life scenarios, such systems are often urgently needed in helping users accomplishing different tasks under various situations. However, existing works still face several shortcomings: (1) Most efforts are largely confined in single task setting. (2) The conversational recommender naturally has access to the back-end data which should be fully leveraged to yield good recommendations. In this paper, we thus present a Topic-guided Conversational Recommender which is specifically designed for the multi-domain setting. It augments the sequence-to-sequence models with a neural latent topic component to better guide the response generation. To better leverage the dialogue history and the back-end data structure, we adopt a graph convolutional network to model the relationships between different recommendation candidates while also capture the match between candidates and the dialogue history. We then seamlessly combine these two parts with the idea of pointer networks. Extensive experiments show that our method achieves superior performance as compared to a wide range of baselines.