Developing trust networks based on user tagging information for recommendation making

Bhuiyan, Touhid, Xu, Yue, Josang, Audun, Liang, Huizhi, & Cox, Clive (2010) Developing trust networks based on user tagging information for recommendation making. In Web Information Systems Engineering – WISE 2010 LNCS, Springer, Hong Kong, China, pp. 357-364.

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Recommender systems are one of the recent inventions to deal with ever growing information overload. Collaborative filtering seems to be the most popular technique in recommender systems. With sufficient background information of item ratings, its performance is promising enough. But research shows that it performs very poor in a cold start situation where previous rating data is sparse. As an alternative, trust can be used for neighbor formation to generate automated recommendation. User assigned explicit trust rating such as how much they trust each other is used for this purpose. However, reliable explicit trust data is not always available. In this paper we propose a new method of developing trust networks based on user’s interest similarity in the absence of explicit trust data. To identify the interest similarity, we have used user’s personalized tagging information. This trust network can be used to find the neighbors to make automated recommendations. Our experiment result shows that the proposed trust based method outperforms the traditional collaborative filtering approach which uses users rating data. Its performance improves even further when we utilize trust propagation techniques to broaden the range of neighborhood.

Impact and interest:

10 citations in Scopus
2 citations in Web of Science®
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ID Code: 41442
Item Type: Conference Paper
Refereed: Yes
Keywords: Trust Networks, Interest Similarity, Recommender Systems, Social Networks, Tags
DOI: 10.1007/978-3-642-17616-6_32
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Decision Support and Group Support Systems (080605)
Divisions: Past > Schools > Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2010 Springer
Copyright Statement:

This is the author-version of the work.

Conference proceedings published, by Springer Verlag, will be available via SpringerLink.

Deposited On: 26 Apr 2011 23:01
Last Modified: 30 Jul 2014 00:10

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