Collaborative Filtering Recommender Systems based on Popular Tags
Liang, Huizhi, Xu, Yue, Li, Yuefeng, & Nayak, Richi (2009) Collaborative Filtering Recommender Systems based on Popular Tags. In ADCS 2009 : Proceedings of the Fourteenth Australasian Document Computing Symposium, School of Information Technologies, University of Sydney, University of New South Wales, Sydney.
The social tags in web 2.0 are becoming another important information source to profile users' interests and preferences for making personalized recommendations. However, the uncontrolled vocabulary causes a lot of problems to profile users accurately, such as ambiguity, synonyms, misspelling, low information sharing etc. To solve these problems, this paper proposes to use popular tags to represent the actual topics of tags, the content of items, and also the topic interests of users. A novel user profiling approach is proposed in this paper that first identifies popular tags, then represents users’ original tags using the popular tags, finally generates users’ topic interests based on the popular tags. A collaborative filtering based recommender system has been developed that builds the user profile using the proposed approach. The user profile generated using the proposed approach can represent user interests more accurately and the information sharing among users in the profile is also increased. Consequently the neighborhood of a user, which plays a crucial role in collaborative filtering based recommenders, can be much more accurately determined. The experimental results based on real world data obtained from Amazon.com show that the proposed approach outperforms other approaches.
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|Item Type:||Conference Paper|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > School of Information Technology
|Copyright Owner:||Copyright 2009 The authors.|
|Deposited On:||19 Jan 2010 23:34|
|Last Modified:||29 Feb 2012 14:12|
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