# Shared MF: A privacy-preserving recommendation system

arXiv: https://arxiv.org/abs/2008.07759 · HTML: https://mlsanity.com/paper/2008.07759/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [A Survey on Federated Recommendation Systems](https://mlsanity.com/paper/2301.00767/) (2022)
- [Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation](https://mlsanity.com/paper/2310.11730/) (2023)
- [FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation](https://mlsanity.com/paper/2205.02359/) (2022)
- [FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation](https://mlsanity.com/paper/2305.06272/) (2023)
- [Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective](https://mlsanity.com/paper/2501.03301/) (2025)
- [Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2505.05989/) (2025)
- [Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendation](https://mlsanity.com/paper/2507.01285/) (2025)

## Abstract (arXiv)

Matrix factorization is one of the most commonly used technologies in recommendation system. With the promotion of recommendation system in e-commerce shopping, online video and other aspects, distributed recommendation system has been widely promoted, and the privacy problem of multi-source data becomes more and more important. Based on Federated learning technology, this paper proposes a shared matrix factorization scheme called SharedMF. Firstly, a distributed recommendation system is built, and then secret sharing technology is used to protect the privacy of local data. Experimental results show that compared with the existing homomorphic encryption methods, our method can have faster execution speed without privacy disclosure, and can better adapt to recommendation scenarios with large amount of data.