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Growing interests have been attracted in Conversational Recommender Systems(CRS), which explore user preference through conversational interactions inorder to make appropriate recommendation. However, there is still a lack ofability in existing CRS to (1) traverse multiple reasoning paths overbackground knowledge to introduce relevant items and attributes, and (2)arrange selected entities appropriately under current system intents to controlresponse generation. To address these issues, we propose CR-Walker in thispaper, a model that performs tree-structured reasoning on a knowledge graph,and generates informative dialog acts to guide language generation. The uniquescheme of tree-structured reasoning views the traversed entity at each hop aspart of dialog acts to facilitate language generation, which links how entitiesare selected and expressed. Automatic and human evaluations show that CR-Walkercan arrive at more accurate recommendation, and generate more informative andengaging responses.

CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation