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Toplam kayıt 43, listelenen: 11-20
Privacy-Preserving Naive Bayesian Classifier-Based Recommendations on Distributed Data
(Wiley, 2015)
Data collected for recommendation purposes might be distributed among various e-commerce sites, which can collaboratively provide more accurate predictions. However, because of privacy concerns, they might not want to work ...
A new hybrid recommendation algorithm with privacy
(Wiley-Blackwell, 2012)
Providing accurate and dependable recommendations efficiently while preserving privacy is essential for e-commerce sites to recruit new customers and keep the existing ones. Such sites might be able to increase their sales ...
P2P collaborative filtering with privacy
(Tubitak Scientific & Technical Research Council Turkey, 2010)
With the evolution of the Internet and e-commerce, collaborative filtering (CF) and privacy-preserving collaborative filtering (PPCF) have become popular The goal in CF is to generate predictions with decent accuracy, ...
An Improved Profile-based CF Scheme with Privacy
(IEEE Computer Soc, 2011)
Traditional collaborative filtering (CF) systems widely employing k- nearest neighbor (kNN) algorithms mostly attempt to alleviate the contemporary problem of information overload by generating personalized predictions for ...
Privacy-preserving SOM-based recommendations on horizontally distributed data
(Elsevier Science BV, 2012)
To produce predictions with decent accuracy, collaborative filtering algorithms need sufficient data. Due to the nature of online shopping and increasing amount of online vendors, different customers' preferences about the ...
Shilling Attacks Against Memory-Based Privacy-Preserving Recommendation Algorithms
(Ksii-Kor Soc Internet Information, 2013)
Privacy-preserving collaborative filtering schemes are becoming increasingly popular because they handle the information overload problem without jeopardizing privacy. However, they may be susceptible to shilling or profile ...
Achieving Optimal Privacy in Trust-Aware Social Recommender Systems
(Springer-Verlag Berlin, 2010)
Collaborative filtering (CF) recommenders are subject to numerous shortcomings such as centralized processing, vulnerability to shilling attacks, and most important of all privacy. To overcome these obstacles, researchers ...
Effects of inconsistently masked data using RPT on CF with privacy
(2007)
Randomized perturbation techniques (RPT) are applied to perturb the customers' private data to protect privacy while providing accurate referrals. In the RPT-based collaborative filtering (CF) with privacy schemes, proposed ...
Privacy-preserving hybrid collaborative filtering on cross distributed data
(Springer London LTD, 2012)
Data collected for collaborative filtering (CF) purposes might be cross distributed between two online vendors, even competing companies. Such corporations might want to integrate their data to provide more precise and ...
Improving privacy-preserving NBC-based recommendations by preprocessing
(2010)
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 ...