Research Repository

SemRec: a semantic enhancement framework for tag based recommendation

Xu, Guandong, Gu, Yanhui, Dolog, Peter, Zhang, Yanchun and Kitsuregawa, Masaru (2011) SemRec: a semantic enhancement framework for tag based recommendation. In: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence and the Twenty-Third Innovative Applications of Artificial Intelligence Conference, 7-11 August 2011, San Francisco, California, USA. Association for the Advancement of Artificial Intelligence Press, pp. 1267-1272.

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Abstract

Collaborative tagging services provided by various social web sites become popular means to mark web resources for different purposes such as categorization, expression of a preference and so on. However, the tags are of syntactic na- ture, in a free style and do not reflect semantics, resulting in the problems of redundancy, ambiguity and less semantics. Current tag-based recommender systems mainly take the ex- plicit structural information among users, resources and tags into consideration, while neglec ting the important implicit se- mantic relationships hidden in tagging data. In this study, we propose a Semantic Enhancement Recommendation strategy (SemRec), based on both structural information and semantic information through a unified fusion model. Extensive exper- iments conducted on two real datasets demonstrate the effectiveness of our approaches.

Item Type: Book Section
ISBN: 9781577355076 (set), 9781577355083 (vol. 1), 9781577355090 (vol. 2)
Uncontrolled Keywords: ResPubID22793, social media, social tagging data, tag clusters, data models, semantic extraction, LDA, similarity fusion, hidden topic number
Subjects: FOR Classification > 0801 Artificial Intelligence and Image Processing
FOR Classification > 0804 Data Format
SEO Classification > 8902 Computer Software and Services
Faculty/School/Research Centre/Department > School of Engineering and Science
Depositing User: VUIR
Date Deposited: 22 Aug 2013 05:20
Last Modified: 22 Aug 2013 05:39
URI: http://vuir.vu.edu.au/id/eprint/9812
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Citations in Scopus: 8 - View on Scopus

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