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Toplam kayıt 9, listelenen: 1-9
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 ...
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 ...
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 ...
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 ...
Providing naïve Bayesian classifier-based private recommendations on partitioned data
(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 ...
On Binary Similarity Measures for Privacy-Preserving Top-N Recommendations
(Scitepress, 2010)
Collaborative filtering (CF) algorithms fundamentally depend on similarities between users and/or items to predict individual preferences. There are various binary similarity measures like Kulzinslcy, Sokal-Michener, Yule, ...
Privacy-Preserving Trust-based Recommendations on Vertically Distributed Data
(IEEE Computer Soc, 2011)
Providing recommendations on trusts between entities is receiving increasing attention lately. Customers may prefer different online vendors for shopping. Thus, their preferences about various products might be distributed ...
Similar or Dissimilar Users? Or Both?
(IEEE Computer Soc, 2009)
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 ...