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Toplam kayıt 12, listelenen: 1-10
Effects of similarity measures on the quality of predictions
(2013)
Providing accurate predictions efficiently is vital for the success of recommender systems. There are various factors that might affect the quality of the predictions and online performance. Similarity metric used to ...
A new hybrid recommendation algorithm with privacy
(Wiley-Blackwell, 2012)
Providing accurate and dependable recommendations efficiently while preserving privacy is essential for e-commerce sites to recruit new customers and keep the existing ones. Such sites might be able to increase their sales ...
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, ...
Estimating Kriging-based predictions with privacy
(2013)
Kriging is a well-known prediction method. It interpolates the value of an unmeasured location from nearby measured locations. In a traditional Kriging interpolation, a client (an entity that is looking for a prediction ...
Privacy-preserving hybrid collaborative filtering on cross distributed data
(Springer London LTD, 2012)
Data collected for collaborative filtering (CF) purposes might be cross distributed between two online vendors, even competing companies. Such corporations might want to integrate their data to provide more precise and ...
Providing predictions on distributed HMMs with privacy
(Springer, 2007)
As forecasting is increasingly becoming important, hidden Markov models (HMMs) are widely used for prediction in many applications such as finance, marketing, bioinformatics, speech recognition, and so on. After creating ...
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 ...
Arbitrarily distributed data-based recommendations with privacy
(Elsevier Science BV, 2012)
Collaborative filtering (CF) systems use customers' preferences about various products to offer recommendations. Providing accurate and reliable predictions is vital for both e-commerce companies and their customers. To ...
Privacy-Preserving Inverse Distance Weighted Interpolation
(Springer Heidelberg, 2014)
Inverse distance weighted (IDW) interpolation is one of the well-known geo-statistics techniques. On the one hand, one party (server) holding some measurements for specific locations wants to provide predictions; on the ...
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. ...