  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.
