# Contrastive Learning for Recommender System

arXiv: https://arxiv.org/abs/2101.01317 · HTML: https://mlsanity.com/paper/2101.01317/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2021.

## Built on by (most cited first, 16 of 16)

- [Graph Self-Supervised Learning: A Survey](https://mlsanity.com/paper/2103.00111/) (2021)
- [Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language](https://mlsanity.com/paper/2303.03363/) (2023)
- [Towards Graph Contrastive Learning: A Survey and Beyond](https://mlsanity.com/paper/2405.11868/) (2024)
- [Embedding in Recommender Systems: A Survey](https://mlsanity.com/paper/2310.18608/) (2023)
- [Graph Soft-Contrastive Learning via Neighborhood Ranking](https://mlsanity.com/paper/2209.13964/) (2022)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering](https://mlsanity.com/paper/2305.12768/) (2023)
- [Robust Training Objectives Improve Embedding-based Retrieval in Industrial Recommendation Systems](https://mlsanity.com/paper/2409.14682/) (2024)
- [Enhancing Travel Decision-Making: A Contrastive Learning Approach for Personalized Review Rankings in Accommodations](https://mlsanity.com/paper/2407.00787/) (2024)
- [Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction](https://mlsanity.com/paper/2109.14766/) (2021)
- [FELRec: Efficient Handling of Item Cold-Start With Dynamic Representation Learning in Recommender Systems](https://mlsanity.com/paper/2210.16928/) (2022)
- [Robust Reinforcement Learning Objectives for Sequential Recommender Systems](https://mlsanity.com/paper/2305.18820/) (2023)
- [General Item Representation Learning for Cold-start Content Recommendations](https://mlsanity.com/paper/2404.13808/) (2024)
- [Dual-Channel Latent Factor Analysis Enhanced Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2408.04838/) (2024)
- [Multiview graph dual-attention deep learning and contrastive learning for multi-criteria recommender systems](https://mlsanity.com/paper/2502.19271/) (2025)
- [Generalizing Linear Autoencoder Recommenders with Decoupled Expected Quadratic Loss](https://mlsanity.com/paper/2603.07402/) (2026)

## Abstract (arXiv)

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.