Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
3-2025
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 useritem 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.
Keywords
Recommendation systems, collaborative filtering, self-supervised learning, contrastive learning, review-based recommender
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Areas of Excellence
Digital transformation
Publication
WSDM '25: Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, Hannover, Germany, 2025 March 10-14
First Page
117
Last Page
126
ISBN
9798400713293
Identifier
10.1145/3701551.3703530
Publisher
ACM
City or Country
New York
Citation
DONG, Viet Hoang; FANG, Yuan; and LAUW, Hady Wirawan.
A contrastive framework with user, item and review alignment for recommendation. (2025). WSDM '25: Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, Hannover, Germany, 2025 March 10-14. 117-126.
Available at: https://ink.library.smu.edu.sg/sis_research/10144
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3701551.3703530