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Toplam kayıt 10, listelenen: 1-10
Robustness analysis of multi-criteria collaborative filtering algorithms against shilling attacks
(Pergamon-Elsevier Science LTD, 2019)
Collaborative filtering is an emerging recommender system technique that aims guiding users based on other customers preferences with behavioral similarities. Such correspondences are located based on preference history ...
Robustness analysis of privacy-preserving model-based recommendation schemes
(Pergamon-Elsevier Science LTD, 2014)
Privacy-preserving model-based recommendation methods are preferable over privacy-preserving memory-based schemes due to their online efficiency. Model-based prediction algorithms without privacy concerns have been ...
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 ...
Improving Accuracy of Multi-Criteria Collaborative Filtering By Normalizing User Ratings
(2017)
Multi-criteria collaborative filtering schemes allow modeling user preferences in a more detailed manner by collecting ratings on various aspects of a product or service. Although preferences are expressed by numerical ...
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 ...
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, ...
A scalable privacy-preserving recommendation scheme via bisecting k-means clustering
(Elsevier Sci LTD, 2013)
Privacy-preserving collaborative filtering is an emerging web-adaptation tool to cope with information overload problem without jeopardizing individuals' privacy. However, collaborative filtering with privacy schemes ...
A comparison of clustering-based privacy-preserving collaborative filtering schemes
(Elsevier, 2013)
Privacy-preserving collaborative filtering (PPCF) methods designate extremely beneficial filtering skills without deeply jeopardizing privacy. However, they mostly suffer from scalability, sparsity, and accuracy problems. ...
An improved privacy-preserving DWT-based collaborative filtering scheme
(Pergamon-Elsevier Science LTD, 2012)
Collaborative filtering (CF) is one of the most efficient techniques to produce personalized recommendations and to deal with the information overload of modern times. Although CF techniques have immensely useful filtering ...
Maskelenmiş Veriler için Kümeleme-Tabanlı Şilin Atak Tespit Yöntemi
(2016)
İnternet'in yaygınlaşması ile beraber hem ortak filtreleme hem de mahremiyetin korunması artan ilgi görmektedir. Mahremiyeti koruyarak doğru önerileri hızlı bir şekilde kullanıcıya sunmak üzere mahremiyettabanlı ortak ...