An improved privacy-preserving DWT-based collaborative filtering scheme

Abstract

Collaborative filtering (CF) is one of the most efficient techniques to produce personalized recommendations and to deal with the information overload of modern times. Although CF techniques have immensely useful filtering capabilities, many CF systems have challenging problems like scalability, accuracy, and privacy. One approach to enhance scalability of such systems is to apply discrete wavelet transformation (DWT) techniques. DWT-based CF schemes significantly overcome the scalability problem. However, they fail to protect individual users' privacy. Moreover, although such schemes provide accurate predictions, the quality of the recommendations provided by DWT-based CF schemes can be further improved by applying some preprocessing methods. In this study, we propose privacy-preserving schemes to produce accurate predictions based on DWT efficiently without deeply exposing customers' privacy. We also recommend methods to order items before applying DWT to boost accuracy. After evaluating our schemes in terms of privacy and supplementary costs, we perform real data-based experiments to scrutinize the proposed schemes in terms of accuracy. Experimental results show that our privacy-preserving methods are able to offer recommendations with decent accuracy. Moreover, our outcomes show that our methods utilized to sort items improve accuracy. We finally provide some suggestions and explain future works