mlsanityData to 7 Oct 2026

Federated Multi-view Matrix Factorization for Personalized Recommendations

arXiv 2004.04256 ↗First posted 8 Apr 2020cs.LG cs.IR stat.MLcited by 27

Not on the map. Why?

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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Apr 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 6; 2023: 9; 2024: 2; 2025: 3; 2026: 2.

27 papers.

Sort:

  1. 2021Federated Reconstruction: Partially Local Federated Learningcited by 78
  2. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  3. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  4. 2023Semi-decentralized Federated Ego Graph Learning for Recommendationcited by 22
  5. 2021Federated Multi-View Learning for Private Medical Data Integration and Analysiscited by 12
  6. 2021FedMood: Federated Learning on Mobile Health Data for Mood Detectioncited by 10
  7. 2021A Payload Optimization Method for Federated Recommender Systemscited by 10
  8. 2023Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sourcescited by 10
  9. 2023Federated Recommendation with Additive Personalizationcited by 9
  10. 2024Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approachcited by 9
  11. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  12. 2023A Privacy Preserving System for Movie Recommendations Using Federated Learningcited by 6
15 more
  1. 2023Federated Deep Multi-View Clustering with Global Self-Supervisioncited by 5
  2. 2022FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillationcited by 4
  3. 2022Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Predictioncited by 2
  4. 2025Federated Recommender System with Data Valuation for E-commerce Platformcited by 2
  5. 2022A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse datacited by 1
  6. 2022PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systemscited by 1
  7. 2025Personalized Recommendation Models in Federated Settings: A Surveycited by 1
  8. 2022Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matricescited by 0
  9. 2023GNN4FR: A Lossless GNN-based Federated Recommendation Frameworkcited by 0
  10. 2023FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systemscited by 0
  11. 2023FedDMF: Privacy-Preserving User Attribute Prediction using Deep Matrix Factorizationcited by 0
  12. 2024Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendationcited by 0
  13. 2025Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendationcited by 0
  14. 2026Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraintscited by 0
  15. 2026Personalized and Multi-View Representation for Federated Cold-Start Recommendationcited by 0