Mining Users' Opinions based on Item Folksonomy and Taxonomy for Personalized Recommender Systems

, , & (2010) Mining Users' Opinions based on Item Folksonomy and Taxonomy for Personalized Recommender Systems. In Wu, Fan, W, Hsu, W, Liu, B, Webb, G I, Zhang, C, et al. (Eds.) Proceedings of the 10th IEEE International Conference on Data Mining Workshops. IEEE Computer Society, United States, pp. 1128-1135.

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Description

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

Impact and interest:

4 citations in Scopus
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ID Code: 41888
Item Type: Chapter in Book, Report or Conference volume (Conference contribution)
ORCID iD:
Xu, Yueorcid.org/0000-0002-1137-0272
Li, Yuefengorcid.org/0000-0002-3594-8980
Measurements or Duration: 8 pages
Keywords: Folksonomy, Opinion Mining, Personalization, Recommender Systems, Tags, Taxonomy
DOI: 10.1109/ICDMW.2010.163
ISBN: 978-0-7695-4257-7/10
Pure ID: 32165499
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > QUT Faculties & Divisions > Science & Engineering Faculty
Current > Research Centres > Australian Research Centre for Aerospace Automation
Copyright Owner: Consult author(s) regarding copyright matters
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Deposited On: 05 Jun 2011 22:31
Last Modified: 02 Mar 2024 16:34