mlsanityData to 7 Oct 2026

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

arXiv 2309.13384 ↗First posted 23 Sep 2023cs.IR cs.LGcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 36 of its 51 references that are in the map. First posted on arXiv 23 Sep 2023.

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References · 36

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24 more
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