Privacy-preserving item-based recommendations over partitioned data with overlaps

Ibrahim Yakut and Jaideep Vaidya

International Journal of Business Information Systems, 2017, vol. 25, issue 3, 336-351

Abstract: 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.

Keywords: collaborative filtering; privacy; arbitrary partitioning; rating overlaps. (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=84449 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:25:y:2017:i:3:p:336-351

Access Statistics for this article

More articles in International Journal of Business Information Systems from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().