Παρακαλώ χρησιμοποιήστε αυτό το αναγνωριστικό για να παραπέμψετε ή να δημιουργήσετε σύνδεσμο προς αυτό το τεκμήριο: https://ruomo.lib.uom.gr/handle/7000/297
Πλήρης εγγραφή μεταδεδομένων
Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorGeorgiadis, Christos K.-
dc.contributor.authorPolatidis, Nikolaos-
dc.contributor.authorMouratidis, Haralambos-
dc.contributor.authorPimenidis, Elias-
dc.date.accessioned2019-10-30T07:07:18Z-
dc.date.available2019-10-30T07:07:18Z-
dc.date.issued2017-
dc.identifier10.3217/jucs-023-02-0146en_US
dc.identifier.urihttp://dx.doi.org/10.3217/jucs-023-02-0146en_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/297-
dc.description.abstractWith the continuous growth of the Internet and the progress of electronic commerce the issues of product recommendation and privacy protection are becoming increasingly important. Recommender Systems aim to solve the information overload problem by providing accurate recommendations of items to users. Collaborative filtering is considered the most widely used recommendation method for providing recommendations of items or users to other users in online environments. Additionally, collaborative filtering methods can be used with a trust network, thus delivering to the user recommendations from both a database of ratings and from users who the person who made the request knows and trusts. On the other hand, the users are having privacy concerns and are not willing to submit the required information (e.g., ratings for products), thus making the recommender system unusable. In this paper, we propose (a) an approach to product recommendation that is based on collaborative filtering and uses a combination of a ratings network with a trust network of the user to provide recommendations and (b) “neighbourhood privacy” that employs a modified privacy-aware role-based access control model that can be applied to databases that utilize recommender systems. Our proposed approach (1) protects user privacy with a small decrease in the accuracy of the recommendations and (2) uses information from the trust network to increase the accuracy of the recommendations, while, (3) providing privacy-preserving recommendations, as accurate as the recommendations provided without the privacy-preserving approach or the method that increased the accuracy applied.en_US
dc.language.isoenen_US
dc.sourceJournal of Universal Computer Scienceen_US
dc.subjectFRASCATI::Natural sciences::Computer and information sciencesen_US
dc.subject.otherCollaborative filteringen_US
dc.subject.otherTrust Networken_US
dc.subject.otherPrivacyen_US
dc.subject.otherRecommender systemsen_US
dc.titleA method for privacy-preserving collaborative filtering recommendationsen_US
dc.typeArticleen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
local.identifier.volume23en_US
local.identifier.issue2en_US
local.identifier.firstpage146en_US
local.identifier.lastpage166en_US
Εμφανίζεται στις Συλλογές: Τμήμα Εφαρμοσμένης Πληροφορικής

Αρχεία σε αυτό το Τεκμήριο:
Αρχείο Περιγραφή ΜέγεθοςΜορφότυπος 
J43_jucs_23_02_0146_0166_georgiadis.pdf232,44 kBAdobe PDFΠροβολή/Ανοιγμα


Τα τεκμήρια στο Αποθετήριο προστατεύονται από πνευματικά δικαιώματα, εκτός αν αναφέρεται κάτι διαφορετικό.