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

TL;DR

CR-Walker is a novel conversational recommender system that uses tree-structured reasoning on knowledge graphs and dialog acts to improve recommendation accuracy and response informativeness.

Contribution

It introduces a tree-structured reasoning framework combined with dialog acts to enhance knowledge traversal and response control in CRS.

Findings

Achieves more accurate recommendations

Generates more informative responses

Improves engagement in conversations

Abstract

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.