NAÏVE BAYESIAN CLASSIFIER-BASED PRIVATE RECOMMENDATIONS

NAÏVE BAYESIAN CLASSIFIER-BASED PRIVATE RECOMMENDATIONS

PRIVACY-PRESERVING NAÏVE BAYESIAN CLASSIFIER-BASED COLLABORATIVE FILTERING

LAP Lambert Academic Publishing ( 2010-02-22 )

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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 naïve 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 data between two parties without violating their privacy, solutions are provided. And finally, a method is proposed for optimizing privacy-preserving NBC-based CF scheme using k-modes clustering.

Book Details:

ISBN-13:

978-3-8383-4772-1

ISBN-10:

3838347722

EAN:

9783838347721

Book language:

English

By (author) :

Cihan Kaleli
Huseyin Polat

Number of pages:

84

Published on:

2010-02-22

Category:

Informatics, IT