Improving privacy-preserving NBC-based recommendations by preprocessing
2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010, Toronto, Canada, 31 August - 03 September 2010, vol.1, pp.143-147, (Full Text)
- Publication Type: Conference Paper / Full Text
- Volume: 1
- Doi Number: 10.1109/wi-iat.2010.109
- City: Toronto
- Country: Canada
- Page Numbers: pp.143-147
- Keywords: Accuracy, Bayesian classifier, Collaborative filtering, Online performance, Preprocessing, Privacy
- Anadolu University Affiliated: Yes
Abstract
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 naïve 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. © 2010 IEEE.