Similar or Dissimilar Users? Or Both?
E-commerce sites utilize collaborative filtering (CF) techniques to offer recommendations to their customers. To recruit new customers and keep the current ones, it is imperative for online vendors to provide accurate predictions efficiently without deeply violating users privacy. To improve the overall performance of CF systems, it is important to use the appropriate data.We investigate how to improve naive Bayesian classifier (NBC)-based CF systems online performance. For this purpose, we group users in various clusters so that predictions can be generated on similar or dissimilar; or both groups of users data. Grouping users into clusters makes it possible to utilize smaller amount of data. We perform real data-based experiments to assess how overall performance changes with different data. Our results show that online time to generate referrals improves significantly when clustering is utilized to get proper data.
collaborative filtering e-commerce performance clustering privacy
Cihan Kaleli Huseyin Polat
Department of Computer Engineering Anadolu University Eskisehir, 26470, Turkey
国际会议
Second International Symposium on Electronic Commerce and Security(第二届电子商务与安全国际研究大会)(ISECS 2009)
南昌
英文
840-845
2009-05-22（万方平台首次上网日期，不代表论文的发表时间）