Contrastive Learning for Recommender System

TL;DR

This paper introduces a contrastive learning framework for recommender systems that enhances embedding quality and reduces sampling bias, improving performance on benchmark datasets.

Contribution

It proposes a novel graph contrastive learning module and a debiased contrastive loss to address dropout randomness and sampling bias in GNN-based recommenders.

Findings

Improved recommendation accuracy on three benchmarks.

Effective reduction of message dropout randomness.

Enhanced negative sampling strategy with bias correction.

Abstract

Recommender systems, which analyze users' preference patterns to suggest potential targets, are indispensable in today's society. Collaborative Filtering (CF) is the most popular recommendation model. Specifically, Graph Neural Network (GNN) has become a new state-of-the-art for CF. In the GNN-based recommender system, message dropout is usually used to alleviate the selection bias in the user-item bipartite graph. However, message dropout might deteriorate the recommender system's performance due to the randomness of dropping out the outgoing messages based on the user-item bipartite graph. To solve this problem, we propose a graph contrastive learning module for a general recommender system that learns the embeddings in a self-supervised manner and reduces the randomness of message dropout. Besides, many recommender systems optimize models with pairwise ranking objectives, such as the Bayesian Pairwise Ranking (BPR) based on a negative sampling strategy. However, BPR has the following problems: suboptimal sampling and sample bias. We introduce a new debiased contrastive loss to solve these problems, which provides sufficient negative samples and applies a bias correction probability to alleviate the sample bias. We integrate the proposed framework, including graph contrastive module and debiased contrastive module with several Matrix Factorization(MF) and GNN-based recommendation models. Experimental results on three public benchmarks demonstrate the effectiveness of our framework.