AdPriRec: a context-aware recommender system for user privacy in MANET services
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
Estimating NBC-based recommendations on arbitrarily partitioned data with privacy
Knowledge-Based Systems
A comparison of clustering-based privacy-preserving collaborative filtering schemes
Applied Soft Computing
A scalable privacy-preserving recommendation scheme via bisecting k-means clustering
Information Processing and Management: an International Journal
| Hi-index | 0.00 |
Providing accurate predictions efficiently with privacy is imperative for both customers and e-commerce vendors. However, privacy, accuracy, and performance are conflicting goals. Although producing referrals with privacy is possible; however, online performance and accuracy degrade due to underlying privacy-preserving measures. We investigate how to improve both efficiency and accuracy of naive Bayesian classifier-based private recommendations by utilizing preprocessing. We preprocess masked data by selecting the best similar items to each item off-line. Moreover, we fill some of the unrated items' cells to improve density. We perform real data-based experiments to investigate how preprocessing affects online performance and accuracy. Our experiment results show that efficiency and preciseness improve due to preprocessing.