Collaborative filtering recommender systems using tag information

Liang, Huizhi, Xu, Yue, Li, Yuefeng, & Nayak, Richi (2008) Collaborative filtering recommender systems using tag information. In Li, Y, Pasi, G, Zhang, C, Cercone, N, & Cao, L (Eds.) Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence, IEEE Computer Society, Australia, New South Wales, Sydney, pp. 59-62.

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Recommender Systems is one of the effective tools to deal with information overload issue. Similar with the explicit rating and other implicit rating behaviours such as purchase behaviour, click streams, and browsing history etc., the tagging information implies user’s important personal interests and preferences information, which can be used to recommend personalized items to users. This paper is to explore how to utilize tagging information to do personalized recommendations. Based on the distinctive three dimensional relationships among users, tags and items, a new user profiling and similarity measure method is proposed. The experiments suggest that the proposed approach is better than the traditional collaborative filtering recommender systems using only rating data.

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19 citations in Scopus
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ID Code: 29734
Item Type: Conference Paper
Refereed: Yes
Keywords: collaborative filtering, collaborative tagging, recommender systems, user profiling
DOI: 10.1109/WIIAT.2008.97
ISBN: 9780769534961
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > School of Information Technology
Past > Schools > School of Information Systems
Copyright Owner: Copyright 2008 IEEE
Deposited On: 19 Jan 2010 22:25
Last Modified: 21 Dec 2016 03:04

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