A Contrastive Framework with User, Item and Review Alignment for Recommendation

TL;DR

This paper proposes ReCAFR, a contrastive learning framework that aligns user, item, and review representations to improve recommendation accuracy, especially in sparse data scenarios.

Contribution

ReCAFR introduces a novel review-centric contrastive approach that integrates reviews into the core user-item space, addressing limitations of previous review-aware models.

Findings

ReCAFR outperforms baseline models on benchmark datasets.

The framework effectively alleviates data sparsity issues.

ReCAFR enhances robustness through self-supervised contrastive strategies.

Abstract

Learning effective latent representations for users and items is the cornerstone of recommender systems. Traditional approaches rely on user-item interaction data to map users and items into a shared latent space, but the sparsity of interactions often poses challenges. While leveraging user reviews could mitigate this sparsity, existing review-aware recommendation models often exhibit two key limitations. First, they typically rely on reviews as additional features, but reviews are not universal, with many users and items lacking them. Second, such approaches do not integrate reviews into the user-item space, leading to potential divergence or inconsistency among user, item, and review representations. To overcome these limitations, our work introduces a Review-centric Contrastive Alignment Framework for Recommendation (ReCAFR), which incorporates reviews into the core learning process, ensuring alignment among user, item, and review representations within a unified space. Specifically, we leverage two self-supervised contrastive strategies that not only exploit review-based augmentation to alleviate sparsity, but also align the tripartite representations to enhance robustness. Empirical studies on public benchmark datasets demonstrate the effectiveness and robustness of ReCAFR.