QUT ePrints

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.

View at publisher

Abstract

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.

Impact and interest:

11 citations in Scopus
Search Google Scholar™

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

713 since deposited on 19 Jan 2010
260 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 29734
Item Type: Conference Paper
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: 20 Jan 2010 08:25
Last Modified: 12 Jul 2013 15:45

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page