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Toplam kayıt 43, listelenen: 21-30
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 ...
Privacy-preserving kriging interpolation on partitioned data
(Elsevier Science BV, 2014)
Kriging is well-known, frequently applied method in geo-statistics. Its success primarily depends on the total number of measurements for some sample points. If there are sufficient sample points with measurements, kriging ...
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 ...
Privacy-Preserving Two-Party Collaborative Filtering on Overlapped Ratings
(Ksii-Kor Soc Internet Information, 2014)
To promote recommendation services through prediction quality, some privacy-preserving collaborative filtering solutions are proposed to make e-commerce parties collaborate on partitioned data. It is almost probable that ...
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 ...
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 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 Concordance-based Recommendations on Vertically Distributed Data
(IEEE, 2012)
Recommender systems are attractive components of e-commerce. Customers apply such systems to get help for choosing the appropriate product to purchase. To provide accurate and dependable referrals, recommender systems ...
On the Privacy of Horizontally Partitioned Binary Data-Based Privacy-Preserving Collaborative Filtering
(Springer Int Publishing Ag, 2016)
Collaborative filtering systems provide recommendations for their users. Privacy is not a primary concern in these systems; however, it is an important element for the true user participation. Privacy-preserving collaborative ...
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 ...