Harnessing the power of social bookmarking for improving tag-based recommendations.
Social bookmarking and tagging has emerged a new era in user collaboration. Collaborative Tagging allows users to annotate content of their liking, which via the appropriate algorithms can render useful for the provision of product recommendations. It is the case today for tag-based algorithms to wo...
| Publicado en: | Computers in Human Behavior Vol. 50; pp. 239 - 252 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Elsevier B.V.
Sep2015
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=102981915&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 102981915 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07475632 JC4 jtl: Computers in Human Behavior issn: 07475632 maglogo: N pubinfo: dt: Sep2015 vid: 50 pid: 2410 pub: Elsevier B.V. artinfo: ui: 102981915 10.1016/j.chb.2015.03.045 ppf: 239 ppct: 13 formats: tig: atl: Harnessing the power of social bookmarking for improving tag-based recommendations. aug: au: Pitsilis, Georgios Wang, Wei affil: Computer Science Research, Athens, Greece School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China su: Interprofessional relations Social networks World Wide Web Algorithms Application software sug: subj: Interprofessional relations Social networks World Wide Web Software publishers (except video game publishers) Software Publishers Custom Computer Programming Services Other Individual and Family Services Internet Publishing and Broadcasting and Web Search Portals Algorithms Application software keyword: Affinity propagation citeUlike Clustering Collaborative tagging Recommender systems Taxonomy Affinity propagation citeUlike Clustering Collaborative tagging Recommender systems Taxonomy ab: Social bookmarking and tagging has emerged a new era in user collaboration. Collaborative Tagging allows users to annotate content of their liking, which via the appropriate algorithms can render useful for the provision of product recommendations. It is the case today for tag-based algorithms to work complementary to rating-based recommendation mechanisms to predict the user liking to various products. In this paper we propose an alternative algorithm for computing personalized recommendations of products, that uses exclusively the tags provided by the users. Our approach is based on the idea of using the semantic similarity of the user-provided tags for clustering them into groups of similar meaning. Afterwards, some measurable characteristics of users’ Annotation Competency are combined with other metrics, such as user similarity, for computing predictions. The evaluation on data used from a real-world collaborative tagging system, citeUlike , confirmed that our approach outperforms the baseline Vector Space model, as well as other state of the art algorithms, predicting the user liking more accurately. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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