  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.
