Leveraging Knowledge Graph Embedding for Effective Conversational Recommendation

TL;DR

This paper introduces KG-CRS, a knowledge graph-based conversational recommender system that dynamically models relationships among users, items, and attributes to improve recommendation accuracy and conversational quality.

Contribution

It proposes a novel dynamic graph embedding approach integrating user-item and item-attribute relationships for better conversational recommendations.

Findings

Outperforms state-of-the-art methods on three real datasets.

Effectively models relationships among users, items, and attributes.

Improves both recommendation accuracy and conversational relevance.

Abstract

Conversational recommender system (CRS), which combines the techniques of dialogue system and recommender system, has obtained increasing interest recently. In contrast to traditional recommender system, it learns the user preference better through interactions (i.e. conversations), and then further boosts the recommendation performance. However, existing studies on CRS ignore to address the relationship among attributes, users, and items effectively, which might lead to inappropriate questions and inaccurate recommendations. In this view, we propose a knowledge graph based conversational recommender system (referred as KG-CRS). Specifically, we first integrate the user-item graph and item-attribute graph into a dynamic graph, i.e., dynamically changing during the dialogue process by removing negative items or attributes. We then learn informative embedding of users, items, and attributes by also considering propagation through neighbors on the graph. Extensive experiments on three real datasets validate the superiority of our method over the state-of-the-art approaches in terms of both the recommendation and conversation tasks.