On the Sweet Spot of Contrastive Views for Knowledge-enhanced Recommendation

TL;DR

This paper introduces a novel contrastive learning framework that effectively integrates knowledge graphs into recommendation systems by balancing two contrastive views, leading to improved performance on real-world datasets.

Contribution

It proposes a new method that constructs separate contrastive views for KG and IG and fuses KG information into IG to enhance recommendation accuracy.

Findings

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

Demonstrates improved recommendation effectiveness and efficiency

Effectively balances contrastive views for better knowledge integration

Abstract

In recommender systems, knowledge graph (KG) can offer critical information that is lacking in the original user-item interaction graph (IG). Recent process has explored this direction and shows that contrastive learning is a promising way to integrate both. However, we observe that existing KG-enhanced recommenders struggle in balancing between the two contrastive views of IG and KG, making them sometimes even less effective than simply applying contrastive learning on IG without using KG. In this paper, we propose a new contrastive learning framework for KG-enhanced recommendation. Specifically, to make full use of the knowledge, we construct two separate contrastive views for KG and IG, and maximize their mutual information; to ease the contrastive learning on the two views, we further fuse KG information into IG in a one-direction manner.Extensive experimental results on three real-world datasets demonstrate the effectiveness and efficiency of our method, compared to the state-of-the-art. Our code is available through the anonymous link:https://figshare.com/articles/conference_contribution/SimKGCL/22783382