Ara
Toplam kayıt 10, listelenen: 1-10
Robustness analysis of arbitrarily distributed data-based recommendation methods
(Pergamon-Elsevier Science LTD, 2016)
Due to different shopping routines of people, rating preferences of many customers might be partitioned between two parties. Since two different e-companies might sell products from the same range to the identical set of ...
SOM-based recommendations with privacy on multi-party vertically distributed data
(Palgrave Macmillan LTD, 2012)
Data collected for providing recommendations can be partitioned among different parties. Offering distributed data-based predictions is popular due to mutual advantages. It is almost impossible to present trustworthy ...
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 ...
An entropy-based neighbor selection approach for collaborative filtering
(Elsevier, 2014)
Collaborative filtering is an emerging technology to deal with information overload problem guiding customers by offering recommendations on products of possible interest. Forming neighborhood of a user/item is the crucial ...
Privacy-Preserving Random Projection-Based Recommendations Based on Distributed Data
(World Scientific Publ Co Pte LTD, 2013)
Providing recommendations based on distributed data has received an increasing amount of attention because it offers several advantages. Online vendors who face problems caused by a limited amount of available data want ...
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 ...
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, ...
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 ...
Shilling attacks against recommender systems: a comprehensive survey
(Springer, 2014)
Online vendors employ collaborative filtering algorithms to provide recommendations to their customers so that they can increase their sales and profits. Although recommendation schemes are successful in e-commerce sites, ...