A semantic relation-aware deep neural network model for end-to-end conversational recommendation

Abstract

Conversational recommendation system (CRS) aims at recommending appropriate items to the user through a multi-turn conversation. The end-to-end CRS is a type of CRS that models the recommendation task and the conversation task simultaneously which has attracted more and more attention in recent years. At the same time, knowledge graph and Transformer are incorporated into the end-to-end CRS to generate better recommendations and better responses to the user, which makes the CRS have state-of-the-art performance. It is known that there exist semantic relations in a conversation. However, we observe that existing end-to-end CRSs in general ignore the semantic relations in the conversation and therefore would likely hinder the performance of CRSs. Motivated by this, we propose a gated cross- and self-attention based CRS utilizing semantic relation information (ASR) model, which can explicitly model and utilize the semantic relations in a conversation. To the best of our knowledge, we are the first to advocate for modelling and utilizing the semantic relations in the end-to-end CRS, which could help to improve the performance of the CRS. Furthermore, to mitigate the class-imbalance problem that most end-to-end CRSs face, we propose a new negative sampling method which could make the proposed CRS learn better. Moreover, we design a Transformer-based dialogue module integrating the semantic relations in a conversation to generate more diversified and precise responses. Extensive experiments on widely used benchmark datasets demonstrate that the proposed ASR model achieves state-of-the-art results in both recommendation and conversation tasks.