# Contrastive Learning Augmented Social Recommendations

arXiv: https://arxiv.org/abs/2502.15695 · HTML: https://mlsanity.com/paper/2502.15695/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 3 Jan 2025.

## Builds on (most cited first, 14 of 14)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Self-supervised Graph Learning for Recommendation](https://mlsanity.com/paper/2010.10783/) (2020)
- [LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2302.08191/) (2023)
- [A Neural Influence Diffusion Model for Social Recommendation](https://mlsanity.com/paper/1904.10322/) (2019)
- [Dynamic Coattention Networks For Question Answering](https://mlsanity.com/paper/1611.01604/) (2016)
- [When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation](https://mlsanity.com/paper/2305.10822/) (2023)
- [STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation](https://mlsanity.com/paper/2306.12232/) (2023)
- [Graph Bottlenecked Social Recommendation](https://mlsanity.com/paper/2406.08214/) (2024)
- [On Modeling Long-Term User Engagement from Stochastic Feedback](https://mlsanity.com/paper/2302.06101/) (2023)
- [Neighbor Based Enhancement for the Long-Tail Ranking Problem in Video Rank Models](https://mlsanity.com/paper/2302.08128/) (2023)
- [Social4Rec: Distilling User Preference from Social Graph for Video Recommendation in Tencent](https://mlsanity.com/paper/2302.09971/) (2023)

## Abstract (arXiv)

Recommender systems are essential for modern content platforms, yet traditional behavior-based models often struggle with cold users who have limited interaction data. Engaging these users is crucial for platform growth. To bridge this gap, we propose leveraging the social-relation graph to enrich interest representations from behavior-based models. However, extracting value from social graphs is challenging due to relation noise and cross-domain inconsistency. To address the noise propagation and obtain accurate social interest, we employ a dual-view denoising strategy, employing low-rank SVD to the user-item interaction matrix for a denoised social graph and contrastive learning to align the original and reconstructed social graphs. Addressing the interest inconsistency between social and behavioral interests, we adopt a "mutual distillation" technique to isolate the original interests into aligned social/behavior interests and social/behavior specific interests, maximizing the utility of both. Experimental results on widely adopted industry datasets verify the method's effectiveness, particularly for cold users, offering a fresh perspective for future research. The implementation can be accessed at https://github.com/WANGLin0126/CLSRec.