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Epidemic trust-based recommender systems
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0003-2988-8464
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0002-4722-0823
2012 (English)In: Proceedings - 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust and 2012 ASE/IEEE International Conference on Social Computing, SocialCom/PASSAT 2012, IEEE , 2012, 461-470 p.Conference paper, Published paper (Refereed)
Abstract [en]

Collaborative filtering(CF) recommender systems are among the most popular approaches to solving the information overload problem in social networks by generating accurate predictions based on the ratings of similar users. Traditional CF recommenders suffer from lack of scalability while decentralized CF recommenders (DHT-based, Gossip-based etc.) have promised to alleviate this problem. Thus, in this paper we propose a decentralized approach to CF recommender systems that uses the T-Man algorithm to create and maintain an overlay network that in turn would facilitate the generation of recommendations based on local information of a node. We analyse the influence of the number of rounds and neighbors on the accuracy of prediction and item coverage and we propose a new approach to inferring trust values between a user and its neighbors. Our experiment son two datasets show an improvement of prediction accuracy relative to previous approaches while using a highly scalable, decentralized paradigm. We also analyse item coverage and show that our system is able to generate predictions for significant fraction of the users, which is comparable with the centralized approaches.

Place, publisher, year, edition, pages
IEEE , 2012. 461-470 p.
Keyword [en]
Accuracy, Equations, Measurement, Peer to peer computing, Prediction algorithms, Recommender systems, Social network services, collaborative filtering, recommender systems, social networking (online), trusted computing, T-Man algorithm, collaborative filtering recommender systems, decentralized CF recommenders system, decentralized paradigm, epidemic trust-based recommender systems, prediction accuracy, social networks, trust values
National Category
Information Systems
Identifiers
URN: urn:nbn:se:kth:diva-118468DOI: 10.1109/SocialCom-PASSAT.2012.94Scopus ID: 2-s2.0-84873681802ISBN: 978-076954848-7 (print)OAI: oai:DiVA.org:kth-118468DiVA: diva2:606401
Conference
2012 ASE/IEEE International Conference on Social Computing, SocialCom 2012 and the 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2012; Amsterdam; 3 September 2012 through 5 September 2012
Note

QC 20130226

Available from: 2013-02-19 Created: 2013-02-19 Last updated: 2018-01-11Bibliographically approved

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Magureanu, StefanMatskin, Mihhail

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