Conversational recommender systems (CRS) aim to timely trace the dynamic interests of users through dialogues and generate relevant responses for item recommendations. Recently, various external knowledge bases (especially knowledge graphs) are incorporated into CRS to enhance the understanding of conversation contexts. However, recent reasoning-based models heavily rely on simplified structures such as linear structures or fixed-hierarchical structures for causality reasoning, hence they cannot fully figure out sophisticated relationships among utterances with external knowledge. To address this, we propose a novel Tree structure Reasoning schEmA named TREA. TREA constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities, and fully utilizes historical conversations to generate more reasonable and suitable responses for recommended results. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach.

翻译：对话推荐系统旨在通过对话及时追踪用户的动态兴趣，并生成相关回复以进行项目推荐。近期，各类外部知识库（尤其是知识图谱）被融入对话推荐系统以增强对话上下文的理解。然而，现有基于推理的模型过于依赖线性结构或固定层次结构等简化结构进行因果推理，因此无法充分解析包含外部知识的复杂话语间关系。针对此问题，本文提出了一种名为TREA的新型树结构推理模式。TREA构建了多层次可扩展树作为推理结构，以厘清提及实体间的因果关系，并充分利用历史对话生成更合理、适配推荐结果的回复。在两个公开对话推荐数据集上的大量实验验证了该方法的有效性。