On the Sweet Spot of Contrastive Views for Knowledge-enhanced Recommendation
This work addresses a specific bottleneck in knowledge-enhanced recommendation for improving recommendation accuracy, though it appears incremental as it builds on existing contrastive learning methods.
The paper tackles the problem of balancing contrastive views from interaction graphs and knowledge graphs in recommender systems, proposing a new framework that fuses knowledge graph information into the interaction graph and achieves state-of-the-art results on three real-world datasets.
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