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Toplam kayıt 24, listelenen: 11-20
A Multi-Criteria Item-based Collaborative Filtering Framework
(IEEE, 2014)
Collaborative filtering methods are utilized to provide personalized recommendations for users in order to alleviate information overload problem in different domains. Traditional collaborative filtering methods operate ...
Privacy-preserving SOM-based recommendations on horizontally distributed data
(Elsevier Science BV, 2012)
To produce predictions with decent accuracy, collaborative filtering algorithms need sufficient data. Due to the nature of online shopping and increasing amount of online vendors, different customers' preferences about the ...
Achieving Optimal Privacy in Trust-Aware Social Recommender Systems
(Springer-Verlag Berlin, 2010)
Collaborative filtering (CF) recommenders are subject to numerous shortcomings such as centralized processing, vulnerability to shilling attacks, and most important of all privacy. To overcome these obstacles, researchers ...
Shilling attacks against recommender systems: a comprehensive survey
(Springer, 2014)
Online vendors employ collaborative filtering algorithms to provide recommendations to their customers so that they can increase their sales and profits. Although recommendation schemes are successful in e-commerce sites, ...
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 ...
Providing naïve Bayesian classifier-based private recommendations on partitioned data
(2007)
Data collected for collaborative filtering (CF) purposes might be split between various parties. Integrating such data is helpful for both e-companies and customers due to mutual advantageous. However, due to privacy ...
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 ...
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 ...
An entropy-based neighbor selection approach for collaborative filtering
(Elsevier, 2014)
Collaborative filtering is an emerging technology to deal with information overload problem guiding customers by offering recommendations on products of possible interest. Forming neighborhood of a user/item is the crucial ...