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Author Bhuiyan, T. ♦ Yue Xu ♦ Josang, A.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2008
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Location-based social network ♦ Barium ♦ Filtering ♦ Social network services ♦ Communities ♦ Reputation management ♦ Feature extraction ♦ Internet ♦ Data mining ♦ Trust ♦ Recommendation
Abstract The recent emergence of location-based social networking services is revolutionizing Web-based social networking allowing users to share real-life experiences via geo-tagged user-generated multimedia content. One of the key challenges of the Web-based social networks as an information sharing and exchanging channel is how to manage healthy relationships among community users and ensure the quality of the information shared and exchanged within the community, which holds a very significant importance to user satisfaction. Deciding whom and what information to trust is very difficult in environment where the users are unknown to each other. This paper investigates the possibilities of managing trust between the users of a Web-based social network while recommending items to the members of the network. A novel framework is proposed to integrate trust among community members and public reputation of items to recommend the most appropriate items to a user of the network.
Description Author affiliation: Fac. of Inf. Technol., Queensland Univ. of Technol., Brisbane, QLD (Bhuiyan, T.; Yue Xu; Josang, A.)
ISBN 9780769534961
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-12-09
Publisher Place Australia
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 293.15 kB
Page Count 4
Starting Page 107
Ending Page 110


Source: IEEE Xplore Digital Library