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NAIVE BAYESIAN CLASSIFIER-BASED PRIVATE RECOMMENDATIONS   Cihan Kaleli and Huseyin Polat

NAIVE BAYESIAN CLASSIFIER-BASED PRIVATE RECOMMENDATIONS

84 страниц. 2010 год.
LAP Lambert Academic Publishing
Collaborative filtering (CF) has become very popular on the Internet. Although CF systems are widely used, they have various challenges in recommendation process. For better results, such systems need quality data; however, due to privacy concerns, users hesitate to send their private data or they might send false data. CF systems provide referrals on existing databases compromised of ratings recorded from groups of people evaluating various items; sometimes, the systems'' ratings might be split among different parties. The parties may wish to share their data; but they may not want to disclose their data. Online computation time increases with augmenting number of users. In this book, approaches are proposed to overcome challenges for naive Bayesian classifier (NBC)-based CF algorithm. A new scheme is proposed to produce NBC-based recommendations while preserving users'' privacy by utilizing randomized response techniques (RRT). To offer CF services on distributed...
 
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