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Toplam kayıt 24, listelenen: 1-10
Robustness Analysis of Naive Bayesian Classifier-Based Collaborative Filtering
(Springer-Verlag Berlin, 2013)
In this study, binary forms of previously defined basic shilling attack models are proposed and the robustness of naive Bayesian classifier-based collaborative filtering algorithm is examined. Real data-based experiments ...
Providing Private Recommendations on Personal Social Networks
(Springer-Verlag Berlin, 2010)
Personal social networks are recently used to offer recommendations. Due to privacy concerns, privacy protection while generating accurate referrals is imperative. Since accuracy and privacy are conflicting goals, providing ...
Robustness analysis of naïve Bayesian classifier-based collaborative filtering
(Springer Verlag, 2013)
In this study, binary forms of previously defined basic shilling attack models are proposed and the robustness of naïve Bayesian classifierbased collaborative filtering algorithm is examined. Real data-based experiments ...
Providing private recommendations using naive Bayesian classifier
(Springer-Verlag Berlin, 2007)
Today's CF systems fail to protect users' privacy. Without privacy protection, it becomes a challenge to collect sufficient and high quality data for CF. With privacy protection, users feel comfortable to provide more ...
A review on deep learning for recommender systems: challenges and remedies
(Springer, 2019)
Recommender systems are effective tools of information filtering that are prevalent due to increasing access to the Internet, personalization trends, and changing habits of computer users. Although existing recommender ...
Providing naive Bayesian classifier-based private recommendations on partitioned data
(Springer-Verlag Berlin, 2007)
Data collected for collaborative filtering (CF) purposes might be split between various parties. Integrating such data is helpful for both e-companies and customers due to mutual advantageous. However, due to privacy ...
Methods of Privacy Preserving in Collaborative Filtering
(IEEE, 2017)
Privacy considerations of individuals becomes more and more popular issue in recommender systems due to the increasing need for protecting confidential data. Even though users of recommender systems enjoy with personalized ...
A Survey of Privacy-Preserving Collaborative Filtering Schemes
(World Scientific Publ Co Pte LTD, 2013)
With increasing need for preserving confidential data while providing recommendations, privacy-preserving collaborative filtering has been receiving increasing attention. To make data owners feel more comfortable while ...
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 ...
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, ...