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

TL;DR

This study establishes a framework for privacy-preserving recommender systems using the data collaboration analysis of distributed datasets and shows that the privacy-preserving method for rating prediction can improve the prediction accuracy for distributed datasets.

Abstract

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.

Figures (3)

• Figure 1: Frequency distributions of ratings
• Figure 2: Workflow of the methods for analyzing distributed datasets
• Figure 3: RMSE as a function of the number of involved parties