Contrastive Collaborative Filtering for Cold-Start Item Recommendation

TL;DR

This paper introduces CCFCRec, a contrastive learning-based model that improves cold-start item recommendation by leveraging co-occurrence signals to refine collaborative embeddings, outperforming existing methods.

Contribution

The paper proposes a novel contrastive collaborative filtering framework that effectively utilizes co-occurrence signals to enhance cold-start item recommendation performance.

Findings

Outperforms existing cold-start recommendation methods on real datasets.

Effectively rectifies blurry collaborative embeddings using contrastive learning.

Demonstrates theoretical soundness and empirical superiority.

Abstract

The cold-start problem is a long-standing challenge in recommender systems. As a promising solution, content-based generative models usually project a cold-start item's content onto a warm-start item embedding to capture collaborative signals from item content so that collaborative filtering can be applied. However, since the training of the cold-start recommendation models is conducted on warm datasets, the existent methods face the issue that the collaborative embeddings of items will be blurred, which significantly degenerates the performance of cold-start item recommendation. To address this issue, we propose a novel model called Contrastive Collaborative Filtering for Cold-start item Recommendation (CCFCRec), which capitalizes on the co-occurrence collaborative signals in warm training data to alleviate the issue of blurry collaborative embeddings for cold-start item recommendation. In particular, we devise a contrastive collaborative filtering (CF) framework, consisting of a content CF module and a co-occurrence CF module to generate the content-based collaborative embedding and the co-occurrence collaborative embedding for a training item, respectively. During the joint training of the two CF modules, we apply a contrastive learning between the two collaborative embeddings, by which the knowledge about the co-occurrence signals can be indirectly transferred to the content CF module, so that the blurry collaborative embeddings can be rectified implicitly by the memorized co-occurrence collaborative signals during the applying phase. Together with the sound theoretical analysis, the extensive experiments conducted on real datasets demonstrate the superiority of the proposed model. The codes and datasets are available on https://github.com/zzhin/CCFCRec.