Federated Multi-view Matrix Factorization for Personalized Recommendations

By Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy, Kuan Eeik Tan, Suleiman A. Khan, Muhammad Ammad-ud-din. 2020

84 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Marketing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qiang Yang
2. Murali Annavaram
3. Chaoyang He
4. Yang Liu
5. Fangzhao Wu
6. Yongfeng Huang
7. Xing Xie
8. S. Avestimehr

In Information Systems

1. Fangzhao Wu
2. Chuhan Wu
3. Yongfeng Huang
4. Xing Xie
5. Qiang Yang
6. Peilin Zhao
7. Jinhyun So
8. Ramesh Raskar

In Marketing

1. Xing Xie
2. Yang Cao
3. Chuhan Wu
4. Qiang Yang
5. Yongfeng Huang
6. Fangzhao Wu

Citations in the corpus

• FedML: A Research Library and Benchmark for Federated Machine Learning (2020, background): “…computer vision [77, 78], natural language processing [79, 80, 81, 82, 83], finance [74, 35, 84], transportation [85, 86, 87, 88, 89, 90, 91, 92, 93, 94], digital health [95, 96, 97, 98, 99, 100, 101], recommendation [102, 103, 104, 105, 106, 107], robotics [108, 109], and smart cities [110, 111].”
• A federated graph neural network framework for privacy-preserving personalization (2021, methodology): “The framework of federated learning has been applied to personalized recommendation [1, 3, 6, 9, 22].”
• Federated Multi-view Learning for Private Medical Data Integration and Analysis (2021, background): “integrated multi-view matrix factorization with a federated learning framework for personalized recommendations and introduced a solution to the cold-start problem [19].”