  Growing interests have been attracted in Conversational Recommender Systems
(CRS), which explore user preference through conversational interactions in
order to make appropriate recommendation. However, there is still a lack of
ability in existing CRS to (1) traverse multiple reasoning paths over
background knowledge to introduce relevant items and attributes, and (2)
arrange selected entities appropriately under current system intents to control
response generation. To address these issues, we propose CR-Walker in this
paper, a model that performs tree-structured reasoning on a knowledge graph,
and generates informative dialog acts to guide language generation. The unique
scheme of tree-structured reasoning views the traversed entity at each hop as
part of dialog acts to facilitate language generation, which links how entities
are selected and expressed. Automatic and human evaluations show that CR-Walker
can arrive at more accurate recommendation, and generate more informative and
engaging responses.
