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Toplam kayıt 16, listelenen: 11-16
On the Privacy of Horizontally Partitioned Binary Data-Based Privacy-Preserving Collaborative Filtering
(Springer Int Publishing Ag, 2016)
Collaborative filtering systems provide recommendations for their users. Privacy is not a primary concern in these systems; however, it is an important element for the true user participation. Privacy-preserving collaborative ...
Randomization-based Privacy-preserving Frameworks for Collaborative Filtering
(Elsevier Science BV, 2016)
Randomization-based privacy protection methods are widely used in collaborative filtering systems to achieve individual privacy. The basic idea behind randomization utilized in collaborative filtering schemes is to add ...
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 ...
Privacy-Preserving Collaborative Filtering on Overlapped Ratings
(IEEE Computer Soc, 2013)
To promote recommendation services through prediction quality, there are some privacy-preserving collaborative filtering (PPCF) solutions enabling e-commerce parties to collaborate on partitioned data. It is almost probable ...
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 ...