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.
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|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).|
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|Deposited On:||06 Jun 2011 04:28|
|Last Modified:||01 Mar 2012 03:33|
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