Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation

TL;DR

This paper introduces MCCL, a contrastive learning-based matrix completion method that enhances robustness and accuracy in recommender systems by denoising and aligning graph representations.

Contribution

The paper proposes a novel contrastive learning framework for matrix completion that combines denoising and autoencoder-based graph representations to improve robustness and generalization.

Findings

Achieves up to 0.8% RMSE improvement on real datasets.

Improves ranking metrics by up to 36%.

Enhances model robustness against noisy or irrelevant edges.

Abstract

Matrix completion is a widely adopted framework in recommender systems, as predicting the missing entries in the user-item rating matrix enables a comprehensive understanding of user preferences. However, current graph neural network (GNN)-based approaches are highly sensitive to noisy or irrelevant edges--due to their inherent message-passing mechanisms--and are prone to overfitting, which limits their generalizability. To overcome these challenges, we propose a novel method called Matrix Completion using Contrastive Learning (MCCL). Our approach begins by extracting local neighborhood subgraphs for each interaction and subsequently generates two distinct graph representations. The first representation emphasizes denoising by integrating GNN layers with an attention mechanism, while the second is obtained via a graph variational autoencoder that aligns the feature distribution with a standard prior. A mutual learning loss function is employed during training to gradually harmonize these representations, enabling the model to capture common patterns and significantly enhance its generalizability. Extensive experiments on several real-world datasets demonstrate that our approach not only improves the numerical accuracy of the predicted scores--achieving up to a 0.8% improvement in RMSE--but also produces superior rankings with improvements of up to 36% in ranking metrics.