# Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation

arXiv: https://arxiv.org/abs/1711.11458 · HTML: https://mlsanity.com/paper/1711.11458/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2017.

## Built on by (most cited first, 8 of 8)

- [Causal Matrix Completion](https://mlsanity.com/paper/2109.15154/) (2021)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [Correcting the User Feedback-Loop Bias for Recommendation Systems](https://mlsanity.com/paper/2109.06037/) (2021)
- [Extracting Attentive Social Temporal Excitation for Sequential Recommendation](https://mlsanity.com/paper/2109.13539/) (2021)
- [Dual-embedding based Neural Collaborative Filtering for Recommender Systems](https://mlsanity.com/paper/2102.02549/) (2021)
- [Automatic Debiased Learning from Positive, Unlabeled, and Exposure Data](https://mlsanity.com/paper/2303.04797/) (2023)
- [Preference or Intent? Double Disentangled Collaborative Filtering](https://mlsanity.com/paper/2305.11084/) (2023)

## Abstract (arXiv)

This paper is concerned with how to make efficient use of social information to improve recommendations. Most existing social recommender systems assume people share similar preferences with their social friends. Which, however, may not hold true due to various motivations of making online friends and dynamics of online social networks. Inspired by recent causal process based recommendations that first model user exposures towards items and then use these exposures to guide rating prediction, we utilize social information to capture user exposures rather than user preferences. We assume that people get information of products from their online friends and they do not have to share similar preferences, which is less restrictive and seems closer to reality. Under this new assumption, in this paper, we present a novel recommendation approach (named SERec) to integrate social exposure into collaborative filtering. We propose two methods to implement SERec, namely social regularization and social boosting, each with different ways to construct social exposures. Experiments on four real-world datasets demonstrate that our methods outperform the state-of-the-art methods on top-N recommendations. Further study compares the robustness and scalability of the two proposed methods.