# Federated Multi-view Matrix Factorization for Personalized Recommendations

arXiv: https://arxiv.org/abs/2004.04256 · HTML: https://mlsanity.com/paper/2004.04256/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2020.

## Built on by (most cited first, 27 of 27)

- [Federated Reconstruction: Partially Local Federated Learning](https://mlsanity.com/paper/2102.03448/) (2021)
- [Federated Social Recommendation with Graph Neural Network](https://mlsanity.com/paper/2111.10778/) (2021)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Semi-decentralized Federated Ego Graph Learning for Recommendation](https://mlsanity.com/paper/2302.10900/) (2023)
- [Federated Multi-View Learning for Private Medical Data Integration and Analysis](https://mlsanity.com/paper/2105.01603/) (2021)
- [FedMood: Federated Learning on Mobile Health Data for Mood Detection](https://mlsanity.com/paper/2102.09342/) (2021)
- [A Payload Optimization Method for Federated Recommender Systems](https://mlsanity.com/paper/2107.13078/) (2021)
- [Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources](https://mlsanity.com/paper/2305.06217/) (2023)
- [Federated Recommendation with Additive Personalization](https://mlsanity.com/paper/2301.09109/) (2023)
- [Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach](https://mlsanity.com/paper/2408.08931/) (2024)
- [Advancements in Federated Learning: Models, Methods, and Privacy](https://mlsanity.com/paper/2302.11466/) (2023)
- [A Privacy Preserving System for Movie Recommendations Using Federated Learning](https://mlsanity.com/paper/2303.04689/) (2023)
- [FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation](https://mlsanity.com/paper/2205.02359/) (2022)
- [Federated Deep Multi-View Clustering with Global Self-Supervision](https://mlsanity.com/paper/2309.13697/) (2023)
- [Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction](https://mlsanity.com/paper/2209.00629/) (2022)
- [Federated Recommender System with Data Valuation for E-commerce Platform](https://mlsanity.com/paper/2509.11196/) (2025)
- [A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data](https://mlsanity.com/paper/2204.07819/) (2022)
- [PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems](https://mlsanity.com/paper/2210.07775/) (2022)
- [Personalized Recommendation Models in Federated Settings: A Survey](https://mlsanity.com/paper/2504.07101/) (2025)
- [Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints](https://mlsanity.com/paper/2604.20031/) (2026)
- [Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices](https://mlsanity.com/paper/2204.07818/) (2022)
- [GNN4FR: A Lossless GNN-based Federated Recommendation Framework](https://mlsanity.com/paper/2308.01197/) (2023)
- [FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems](https://mlsanity.com/paper/2309.08635/) (2023)
- [FedDMF: Privacy-Preserving User Attribute Prediction using Deep Matrix Factorization](https://mlsanity.com/paper/2312.15420/) (2023)
- [Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation](https://mlsanity.com/paper/2411.19678/) (2024)
- [Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendation](https://mlsanity.com/paper/2507.01285/) (2025)
- [Personalized and Multi-View Representation for Federated Cold-Start Recommendation](https://mlsanity.com/paper/2608.27826/) (2026)

## Abstract (arXiv)

We introduce the federated multi-view matrix factorization method that extends the federated learning framework to matrix factorization with multiple data sources. Our method is able to learn the multi-view model without transferring the user's personal data to a central server. As far as we are aware this is the first federated model to provide recommendations using multi-view matrix factorization. The model is rigorously evaluated on three datasets on production settings. Empirical validation confirms that federated multi-view matrix factorization outperforms simpler methods that do not take into account the multi-view structure of the data, in addition, it demonstrates the usefulness of the proposed method for the challenging prediction tasks of cold-start federated recommendations.