Mining Users' Opinions based on Item Folksonomy and Taxonomy for Personalized Recommender Systems
|
Accepted Version
(PDF 562kB)
idcm_opinionMining_cameraReady.pdf. |
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:
Citation counts are 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:
Full-text downloads displays 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: | 41888 | ||||
---|---|---|---|---|---|
Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||||
ORCID iD: |
|
||||
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 | ||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||
Deposited On: | 05 Jun 2011 22:31 | ||||
Last Modified: | 02 Mar 2024 16:34 |
Export: EndNote | Dublin Core | BibTeX
Repository Staff Only: item control page