Privacy-preserving item-based recommendations over partitioned data with overlaps
International Journal of Business Information Systems, vol.25, no.3, pp.336-351, 2017 (Scopus)
- Publication Type: Article / Abstract
- Volume: 25 Issue: 3
- Publication Date: 2017
- Doi Number: 10.1504/ijbis.2017.084449
- Journal Name: International Journal of Business Information Systems
- Journal Indexes: Scopus
- Page Numbers: pp.336-351
- Keywords: Arbitrary partitioning, Collaborative filtering, Privacy, Rating overlaps
- Anadolu University Affiliated: Yes
Abstract
Copyright © 2017 Inderscience Enterprises Ltd.User ratings are vital elements to drive recommender systems and, in the case of an insufficient amount of ratings, companies may prefer to operate recommender services over partitioned data. To make this feasible, there are privacy-preserving schemes. However, such solutions currently have not comprehensively investigated probable rating overlaps among partitioned data. Such overlaps make collaboration over partitioned data more challenging, especially if overlapped values are divergent. In this study, we examine this privacy-preserving recommender problem and propose novel schemes in this sense. By means of our schemes, two parties can perform item-based collaborative filtering over partitioned data with divergent overlaps. We also show that the proposed solutions promote prediction quality with tolerable overheads.