# Meta Matrix Factorization for Federated Rating Predictions

arXiv: https://arxiv.org/abs/1910.10086 · HTML: https://mlsanity.com/paper/1910.10086/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 30 of 48)

- [Federated Reconstruction: Partially Local Federated Learning](https://mlsanity.com/paper/2102.03448/) (2021)
- [PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion](https://mlsanity.com/paper/2110.10926/) (2021)
- [Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning](https://mlsanity.com/paper/2102.12920/) (2021)
- [Device-Cloud Collaborative Learning for Recommendation](https://mlsanity.com/paper/2104.06624/) (2021)
- [Semi-decentralized Federated Ego Graph Learning for Recommendation](https://mlsanity.com/paper/2302.10900/) (2023)
- [Dual Personalization on Federated Recommendation](https://mlsanity.com/paper/2301.08143/) (2023)
- [Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training](https://mlsanity.com/paper/2401.03748/) (2024)
- [Vertical Semi-Federated Learning for Efficient Online Advertising](https://mlsanity.com/paper/2209.15635/) (2022)
- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation](https://mlsanity.com/paper/2305.16163/) (2023)
- [Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach](https://mlsanity.com/paper/2408.08931/) (2024)
- [Co-clustering for Federated Recommender System](https://mlsanity.com/paper/2411.01690/) (2024)
- [Memory-adaptive Depth-wise Heterogeneous Federated Learning](https://mlsanity.com/paper/2303.04887/) (2023)
- [VFed-SSD: Towards Practical Vertical Federated Advertising](https://mlsanity.com/paper/2205.15987/) (2022)
- [Personalized Federated Domain Adaptation for Item-to-Item Recommendation](https://mlsanity.com/paper/2306.03191/) (2023)
- [Efficient and Robust Regularized Federated Recommendation](https://mlsanity.com/paper/2411.01540/) (2024)
- [DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation](https://mlsanity.com/paper/2406.08804/) (2024)
- [Cross-Silo Heterogeneous Model Federated Multitask Learning](https://mlsanity.com/paper/2202.08603/) (2022)
- [Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction](https://mlsanity.com/paper/2209.00629/) (2022)
- [Federated Variational Inference: Towards Improved Personalization and Generalization](https://mlsanity.com/paper/2305.13672/) (2023)
- [Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on Convergence](https://mlsanity.com/paper/2311.12358/) (2023)
- [Cluster-Enhanced Federated Graph Neural Network for Recommendation](https://mlsanity.com/paper/2412.08066/) (2024)
- [FedCD: A Fairness-aware Federated Cognitive Diagnosis Framework](https://mlsanity.com/paper/2508.01296/) (2025)
- [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)
- [UFGraphFR: Graph Federation Recommendation System based on User Text description features](https://mlsanity.com/paper/2501.08044/) (2025)
- [Modeling Churn in Recommender Systems with Aggregated Preferences](https://mlsanity.com/paper/2502.18483/) (2025)
- [Personalized Recommendation Models in Federated Settings: A Survey](https://mlsanity.com/paper/2504.07101/) (2025)
- [Federated Latent Factor Learning for Recovering Wireless Sensor Networks Signal with Privacy-Preserving](https://mlsanity.com/paper/2504.15525/) (2025)

## Abstract (arXiv)

Federated recommender systems have distinct advantages in terms of privacy protection over traditional recommender systems that are centralized at a data center. However, previous work on federated recommender systems does not fully consider the limitations of storage, RAM, energy and communication bandwidth in a mobile environment. The scales of the models proposed are too large to be easily run on mobile devices. And existing federated recommender systems need to fine-tune recommendation models on each device, making it hard to effectively exploit collaborative filtering information among users/devices. Our goal in this paper is to design a novel federated learning framework for rating prediction (RP) for mobile environments. We introduce a federated matrix factorization (MF) framework, named meta matrix factorization (MetaMF). Given a user, we first obtain a collaborative vector by collecting useful information with a collaborative memory module. Then, we employ a meta recommender module to generate private item embeddings and a RP model based on the collaborative vector in the server. To address the challenge of generating a large number of high-dimensional item embeddings, we devise a rise-dimensional generation strategy that first generates a low-dimensional item embedding matrix and a rise-dimensional matrix, and then multiply them to obtain high-dimensional embeddings. We use the generated model to produce private RPs for the given user on her device. MetaMF shows a high capacity even with a small RP model, which can adapt to the limitations of a mobile environment. We conduct extensive experiments on four benchmark datasets to compare MetaMF with existing MF methods and find that MetaMF can achieve competitive performance. Moreover, we find MetaMF achieves higher RP performance over existing federated methods by better exploiting collaborative filtering among users/devices.