Privacy-preserving recommender system using the data collaboration analysis for distributed datasets
This addresses privacy concerns for users and organizations in recommender systems, though it appears incremental as it builds on existing data collaboration analysis.
The paper tackles the problem of improving recommendation quality by integrating distributed datasets while protecting personal information, and demonstrates that their privacy-preserving method enhances rating prediction accuracy in experiments with public datasets.
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.