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.
翻译:建议中的人类对话自然涉及利益转移,这种利益转移可以使建议行动和对话过程与建议提出详细解释的准确建议;然而,现有的对话建议系统忽视了用户在将建议和对话联系起来方面的利益转移的优势,从而导致建议和对话的松散结构无效。为了解决这一问题,我们提议通过将建议行动作为建议路径在知识图(KG)中建模,将建议行动建模为建议路径,从而在对话建议中设计一种双重模仿,分别将建议路径和用户利益转移路径与建议模块和对话模块明确统一起来。通过交换协调信号,投资、投资、投资、投资、投资、投资、投资、投资、投资、投资促进在建议和对话模块之间实现双向促进,并产生含有准确建议和连贯解释的高质量回应。实验表明,投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资、投资