Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations with rich explanations. However, existing conversational recommendation systems (CRS) ignore the advantage of user interest shift in connecting recommendation and conversation, which leads to an ineffective loose coupling structure of CRS. To address this issue, by modeling the recommendation actions as recommendation paths in a knowledge graph (KG), we propose DICR (Dual Imitation for Conversational Recommendation), which designs a dual imitation to explicitly align the recommendation paths and user interest shift paths in a recommendation module and a conversation module, respectively. By exchanging alignment signals, DICR achieves bidirectional promotion between recommendation and conversation modules and generates high-quality responses with accurate recommendations and coherent explanations. Experiments demonstrate that DICR outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics.

本文提出DICR，通过将推荐动作建模为知识图谱中的推荐路径，设计了双向模仿来明确地对齐推荐模块和对话模块中的推荐路径和用户兴趣变化路径，从而实现了推荐和对话模块之间的双向促进，并生成具有准确推荐和连贯解释的高质量响应。实验结果表明DICR在推荐和对话性能以及自动化、人类和新颖可解释性指标方面优于现有技术水平。

通过双重模仿实现推荐与对话的对齐