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Toplam kayıt 16, listelenen: 1-10
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, ...
Privacy-Preserving Kriging Interpolation on Distributed Data
(Springer-Verlag Berlin, 2014)
Kriging is one of the most preferred geostatistical methods in many engineering fields. Basically, it creates a model using statistical properties of all measured points in the region, where a prediction value is sought. ...
Effects of Inconsistently Masked Data Using RPT on CF with Privacy
(Assoc Computing Machinery, 2007)
Randomized perturbation techniques (RPT) are applied to perturb the customers' private data to protect privacy while providing accurate referrals. In the RPT-based collaborative filtering (CF) with privacy schemes, proposed ...
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 ...
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 ...
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 ...
Effects of inconsistently masked data using RPT on CF with privacy
(2007)
Randomized perturbation techniques (RPT) are applied to perturb the customers' private data to protect privacy while providing accurate referrals. In the RPT-based collaborative filtering (CF) with privacy schemes, proposed ...
Privacy-preserving kriging interpolation on distributed data
(Springer Verlag, 2014)
Kriging is one of the most preferred geostatistical methods in many engineering fields. Basically, it creates a model using statistical properties of all measured points in the region, where a prediction value is sought. ...
Methods of Privacy Preserving in Collaborative Filtering
(IEEE, 2017)
Privacy considerations of individuals becomes more and more popular issue in recommender systems due to the increasing need for protecting confidential data. Even though users of recommender systems enjoy with personalized ...