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Toplam kayıt 43, listelenen: 31-40
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 ...
Privacy-Preserving Svd-Based Collaborative Filtering on Partitioned Data
(World Scientific Publ Co Pte LTD, 2010)
Collaborative filtering (CF) systems are widely employed by many e-commerce sites for providing recommendations to their customers. To recruit new customers, retain the current ones, and gain competitive edge over competing ...
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 ...
Estimating NBC-based recommendations on arbitrarily partitioned data with privacy
(Elsevier, 2012)
Providing partitioned data-based recommendations has been receiving increasing attention due to mutual advantages. In case of limited data, it is not likely to estimate accurate and reliable predictions. Therefore. e-commerce ...
Detecting shilling attacks in private environments
(Springer, 2016)
Privacy-preserving collaborative filtering algorithms are successful approaches. However, they are susceptible to shilling attacks. Recent research has increasingly focused on collaborative filtering to protect against ...
A survey: deriving private information from perturbed data
(Springer, 2015)
Privacy-preserving data mining has attracted the attention of a large number of researchers. Many data perturbation methods have been proposed to ensure individual privacy. Such methods seem to be successful in providing ...
Privacy-preserving normalized ratings-based weighted slope one predictor
(WITPress, 2016)
Weighted Slope One predictor is proposed as a model-based collaborative filtering algorithm based on user ratings. The predictor is able to efficiently provide accurate predictions. The scheme utilizes user's true ratings. ...
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, ...
Reconstructing rated items from perturbed data
(Elsevier Science BV, 2016)
The basic idea behind privacy-preserving collaborative filtering schemes is to prevent data collectors from deriving the actual rating values and the rated items. Different data perturbation methods have been proposed to ...