Privacy-Aware Cross-Platform Service Recommendation Based on Enhanced Locality-Sensitive Hashing

Abstract

Recommender systems are a promising way for users to quickly find the valuable information that they are interested in from massive data. Concretely, by capturing the user's personalized preferences, a recommender system can return a list of recommended items that best match the user preferences by using collaborative filtering. However, in the big data environment, the heavily fragmented distribution of the QoS (Quality of Services) data for recommendation decision- making presents a large challenge when integrating the QoS data from different platforms while ensuring that the sensitive user information contained in the QoS data is secure. Furthermore, due to the common tradeoff between data availability and privacy in data-driven applications, protecting the sensitive user information contained in the QoS data will probably decrease the availability of QoS data and finally produce inaccurate recommendation results. Considering these challenges, we enhance the classic Locality-Sensitive Hashing (LSH) technique, after which we propose an approach based on enhanced LSH for accurate and less-sensitive cross-platform recommendation decision-makings. Finally, extensive experiments are designed and tested on the reputable WS-DREAM dataset. The test reports prove the benefits of our work compared to other competitive approaches in the aspects of recommendation accuracy, efficiency and privacy protection performances.