Privacy-preserving normalized ratings-based weighted slope one predictor

Abstract

Weighted Slope One predictor is proposed as a model-based collaborative filtering algorithm based on user ratings. The predictor is able to efficiently provide accurate predictions. The scheme utilizes user's true ratings. In this paper, we propose to utilize normalized user ratings like z-scores for the weighted Slope One predictor. Also, in order to protect privacy, we propose a privacy-preserving weighted Slope One predictor based on z-scores using randomization. Moreover, we utilize masked deviations to show how it affects accuracy of the proposed scheme. We perform various real data-based experiments to evaluate the overall performance of the proposed method. Empirical outcomes show that the algorithm is able to provide accurate predictions.