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

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.