# Privacy-preserving recommender system using the data collaboration analysis for distributed datasets

arXiv: https://arxiv.org/abs/2406.01603 · HTML: https://mlsanity.com/paper/2406.01603/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 56 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 16 of 16)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Differential Privacy Has Disparate Impact on Model Accuracy](https://mlsanity.com/paper/1905.12101/) (2019)
- [Deep Learning based Recommender System: A Survey and New Perspectives](https://mlsanity.com/paper/1707.07435/) (2017)
- [Conversational Recommender System](https://mlsanity.com/paper/1806.03277/) (2018)
- [Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System](https://mlsanity.com/paper/1901.09888/) (2019)
- [Federated Social Recommendation with Graph Neural Network](https://mlsanity.com/paper/2111.10778/) (2021)
- [Fast-adapting and Privacy-preserving Federated Recommender System](https://mlsanity.com/paper/2104.00919/) (2021)
- [LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization](https://mlsanity.com/paper/2206.11743/) (2022)
- [Fast Differentially Private Matrix Factorization](https://mlsanity.com/paper/1505.01419/) (2015)
- [ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences](https://mlsanity.com/paper/2207.13897/) (2022)
- [Accuracy and Privacy Evaluations of Collaborative Data Analysis](https://mlsanity.com/paper/2101.11144/) (2021)
- [Another Use of SMOTE for Interpretable Data Collaboration Analysis](https://mlsanity.com/paper/2208.12458/) (2022)
- [Non-readily identifiable data collaboration analysis for multiple datasets including personal information](https://mlsanity.com/paper/2208.14611/) (2022)
- [Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations](https://mlsanity.com/paper/2011.06803/) (2020)
- [New Solutions Based on the Generalized Eigenvalue Problem for the Data Collaboration Analysis](https://mlsanity.com/paper/2404.14164/) (2024)

## Built on by (most cited first, 2 of 2)

- [Robust portfolio optimization for recommender systems considering uncertainty of estimated statistics](https://mlsanity.com/paper/2406.10250/) (2024)
- [Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis](https://mlsanity.com/paper/2605.27219/) (2026)

## Abstract (arXiv)

In order to provide high-quality recommendations for users, it is desirable to share and integrate multiple datasets held by different parties. However, when sharing such distributed datasets, we need to protect personal and confidential information contained in the datasets. To this end, we establish a framework for privacy-preserving recommender systems using the data collaboration analysis of distributed datasets. Numerical experiments with two public rating datasets demonstrate that our privacy-preserving method for rating prediction can improve the prediction accuracy for distributed datasets. This study opens up new possibilities for privacy-preserving techniques in recommender systems.