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User and Document Group Approach of Clustering in Tagging Systems

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dc.contributor.authorPan, Rongde_DE
dc.contributor.authorXu, Guandongde_DE
dc.contributor.authorDolog, Peterde_DE
dc.contributor.editorHartmann, Melaniede_DE
dc.contributor.editorHerder, Eelcode_DE
dc.contributor.editorKrause, Danielde_DE
dc.contributor.editorNauerz, Andreasde_DE
dc.date.accessioned2017-11-15T15:01:00Z
dc.date.available2017-11-15T15:01:00Z
dc.date.issued2010
dc.description.abstractIn this paper, we propose a spectral clustering approach for users and documents group modeling in order to capture the common preference and relatedness of users and documents, and to reduce the time complexity of similarity calculations. In experiments, we investigate the selection of the optimal amount of clusters. We also show a reduction of the time consuming in calculating the similarity for the recommender systems by selecting a centroid first, and then compare the inside item on behalf of each group.
dc.identifier.urihttp://abis.l3s.uni-hannover.de/images/proceedings/abis2010/abis1.pdfde_DE
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/5089
dc.language.isoende_DE
dc.relation.ispartof18th Intl. Workshop on Personalization and Recommendation on the Web and Beyondde_DE
dc.subjectUser Profile
dc.subjectDocument Profile
dc.subjectSpectral Clustering
dc.subjectGroup Profile
dc.subjectModularity Metric
dc.titleUser and Document Group Approach of Clustering in Tagging Systemsde_DE
dc.typeText/Conference Paper
gi.document.qualitydigidoc

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ABIS 2010 – 18th Intl. Workshop on Personalization and Recommendation on the Web and Beyond