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

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 recommen-dation 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. (c) 2022 Elsevier B.V. All rights reserved.