SemRec: A semantic enhancement framework for tag based recommendation

Guandong XU, Yanhui GU, Peter DOLOG, Yanchun ZHANG, Masaru KITSUREGAWA

Research output: Chapter in Book/Report/Conference proceedingChapters

9 Citations (Scopus)

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 nature, 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 explicit structural information among users, resources and tags into consideration, while neglecting the important implicit semantic 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 experiments conducted on two real datasets demonstrate the effectiveness of our approaches. Copyright © 2011 Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Original languageEnglish
Title of host publicationProceedings of the twenty-fifth AAAI conference on artificial intelligence
Place of PublicationUSA
PublisherAAAI press
Pages1267-1272
ISBN (Print)9781577355090
DOIs
Publication statusPublished - 2011

Citation

Xu, G., Gu, Y., Dolog, P., Zhang, Y., & Kitsuregawa, M. (2011). Semrec: SemRec: A semantic enhancement framework for tag based recommendation. In Proceedings of the twenty-fifth AAAI conference on artificial intelligence (pp. 1267-1272). AAAI press. https://doi.org/10.1609/aaai.v25i1.8080

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