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Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems

Liang, Huizhi, Xu, Yue, & Li, Yuefeng (2010) Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems. In Proceedings of 10th IEEE International Conference on Data Mining, IEEE, University of Technology, Sydney, Sydney.

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Abstract

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:

1 citations in Scopus
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ID Code: 41888
Item Type: Conference Paper
Keywords: Recommender Systems, Folksonomy, Tags, Opinion Mining, Personalization, Taxonomy
DOI: 10.1109/ICDMW.2010.163
ISBN: 9781424492442
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2010 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 06 Jun 2011 08:31
Last Modified: 01 Mar 2012 11:43

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