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Toplam kayıt 31, listelenen: 1-10
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 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 ...
Privacy-Preserving Collaborative Filtering on Overlapped Ratings
(IEEE Computer Soc, 2013)
To promote recommendation services through prediction quality, there are some privacy-preserving collaborative filtering (PPCF) solutions enabling e-commerce parties to collaborate on partitioned data. It is almost probable ...
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, ...
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 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. ...
Privacy-preserving item-based recommendations over partitioned data with overlaps
(Inderscience Enterprises Ltd., 2017)
User ratings are vital elements to drive recommender systems and, in the case of an insufficient amount of ratings, companies may prefer to operate recommender services over partitioned data. To make this feasible, there ...
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 ...
Deriving private data in partitioned data-based privacy-preserving collaborative filtering systems
(Gazi University, Fac Engineering Architecture, 2017)
Collaborative filtering algorithms need enough data to provide accurate and reliable predictions. Hence, two e-commerce sites holding insufficient data may want to provide predictions on their partitioned data with privacy. ...
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 ...