Personalized recommender system based on item taxonomy and folksonomy
Liang, Huizhi, Xu, Yue, Li, Yuefeng, & Nayak, Richi (2010) Personalized recommender system based on item taxonomy and folksonomy. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management & Co-Located Workshops, ACM, Fairmont Royal York, Toronto, pp. 1641-1644.
Item folksonomy or tag information is popularly available on the web now. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. In this paper, we propose to combine item taxonomy and folksonomy to reduce the noise of tags and make personalized item recommendations. The experiments conducted on the dataset collected from Amazon.com demonstrated the effectiveness of the proposed approaches. The results suggested that the recommendation accuracy can be further improved if we consider the viewpoints and the vocabularies of both experts and users.
Impact and interest:
Citation countsare sourced monthly fromand citation databases.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Conference Paper|
|Keywords:||Recommender Systems, Folksonomy, Tags, Taxonomy|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Information Engineering and Theory (080607)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright © 2010 by the Association for Computing Machinery, Inc. (ACM).|
|Copyright Statement:||Permission to make digital or hard copies of portions of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyright for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permission to republish from: firstname.lastname@example.org or contact the Publications Dept., ACM, Inc. Fax +1 (212) 869-0481. For other copying of articles that carry a code at the bottom of the first or last page, copying is permitted provided that the per-copy fee indicated in the code is paid through the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923 (USA).|
|Deposited On:||06 Jun 2011 14:28|
|Last Modified:||01 Mar 2012 13:33|
Repository Staff Only: item control page