Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation

TL;DR

This paper introduces KCAN, an end-to-end model that distills and refines knowledge graphs into target-specific subgraphs using conditional attention, improving recommendation performance by capturing personalized knowledge relationships.

Contribution

The paper proposes a novel knowledge-aware conditional attention network that automatically distills target-specific subgraphs from large knowledge graphs for personalized recommendations.

Findings

Outperforms state-of-the-art algorithms on real-world datasets

Effectively captures target-specific knowledge relationships

Enhances recommendation accuracy through personalized graph refinement

Abstract

Knowledge graph is generally incorporated into recommender systems to improve overall performance. Due to the generalization and scale of the knowledge graph, most knowledge relationships are not helpful for a target user-item prediction. To exploit the knowledge graph to capture target-specific knowledge relationships in recommender systems, we need to distill the knowledge graph to reserve the useful information and refine the knowledge to capture the users' preferences. To address the issues, we propose Knowledge-aware Conditional Attention Networks (KCAN), which is an end-to-end model to incorporate knowledge graph into a recommender system. Specifically, we use a knowledge-aware attention propagation manner to obtain the node representation first, which captures the global semantic similarity on the user-item network and the knowledge graph. Then given a target, i.e., a user-item pair, we automatically distill the knowledge graph into the target-specific subgraph based on the knowledge-aware attention. Afterward, by applying a conditional attention aggregation on the subgraph, we refine the knowledge graph to obtain target-specific node representations. Therefore, we can gain both representability and personalization to achieve overall performance. Experimental results on real-world datasets demonstrate the effectiveness of our framework over the state-of-the-art algorithms.