A novel target item-based similarity function in privacy-preserving collaborative filtering

Abstract

AbstractMemory-based collaborative filtering schemes are among the most effective recommendation technologies in terms of prediction quality, despite commonly facing issues related to accuracy, scalability, and privacy. A prominent approach suggests an intuitively reasonable modification to the similarity function, which has been proven to provide more accurate recommendations than those generated by state-of-the-art memory-based collaborative filtering methods. However, this scheme exacerbates the scalability problem due to additional computational costs and fails to protect individual privacy. In this study, we recommend using a preprocessing method to eliminate relatively dissimilar items from the prediction estimation process, thereby enhancing the scalability of the proposed approach. We explore how to provide recommendations based on the previously proposed similarity function while preserving privacy and propose privacy-preserving schemes to accomplish this task. Additionally, we apply our preprocessing approach to our proposed privacy-preserving schemes to improve both scalability and accuracy. After analyzing our schemes with respect to privacy and additional costs, we conduct experiments with real data to examine the impact of our schemes on scalability and accuracy. The empirical outcomes indicate that our preprocessing scheme significantly alleviates scalability issues in both conventional and privacy-preserving environments and enhances accuracy within privacy-preserving frameworks.