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Toplam kayıt 9, listelenen: 1-9
Privacy Risks for Multi-Criteria Collaborative Filtering Systems
(IEEE, 2017)
In case that individuals feel their privacy is violated while using any recommender system, they might be willing to declare incorrect information or even completely refuse to use such services. To relieve customer concerns, ...
The estimation of daily temperature by artificial and fuzzy neural network methods
(2007)
In this study, the estimation of daily temperature of a region by fuzzy logic is discussed and some results are presented. Temperature is an atmospheric parameter which can be estimated by means of some other atmospheric ...
An Improved Profile-based CF Scheme with Privacy
(IEEE Computer Soc, 2011)
Traditional collaborative filtering (CF) systems widely employing k- nearest neighbor (kNN) algorithms mostly attempt to alleviate the contemporary problem of information overload by generating personalized predictions for ...
A Multi-Criteria Item-based Collaborative Filtering Framework
(IEEE, 2014)
Collaborative filtering methods are utilized to provide personalized recommendations for users in order to alleviate information overload problem in different domains. Traditional collaborative filtering methods operate ...
Improving privacy-preserving NBC-based recommendations by preprocessing
(2010)
Providing accurate predictions efficiently with privacy is imperative for both customers and e-commerce vendors. However, privacy, accuracy, and performance are conflicting goals. Although producing referrals with privacy ...
A Robust Multi-Criteria Collaborative Filtering Algorithm
(IEEE, 2018)
Collaborative filtering recommender systems assist individuals to discover relevant products or services that they might be interested in a large set of alternatives by analyzing the collected preferences. Recent research ...
A multi-criteria item-based collaborative filtering framework
(IEEE Computer Society, 2014)
Collaborative filtering methods are utilized to provide personalized recommendations for users in order to alleviate information overload problem in different domains. Traditional collaborative filtering methods operate ...
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, ...
A novel shilling attack detection method
(Elsevier Science BV, 2014)
Recommender systems provide an impressive way to overcome information overload problem. However, they are vulnerable to profile injection or shilling attacks. Malicious users and/or parties might construct fake profiles ...