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dc.contributor.authorClerkin, Patrick
dc.contributor.authorCunningham, Padraig
dc.contributor.authorHayes, Conor
dc.date.accessioned2007-12-12T12:04:52Z
dc.date.available2007-12-12T12:04:52Z
dc.date.issued2003-09-06
dc.identifier.citation[Author surname, forename]. 'Concept Discovery in Collaborative Recommender Systems'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2003-38, 2003, pp6en
dc.identifier.otherTCD-CS-2003-38
dc.identifier.urihttp://hdl.handle.net/2262/12598
dc.description.abstractThere are two main types of recommender systems for e-commerce applications: content-based systems and automated collaborative filtering systems. We are interested in combining the best features of both approaches. In this paper, we investigate the possibility of using the k-means clustering algorithm as a basis for automatically generating content descriptions from the user transaction data that drives the collaborative filtering process. Using the the partitions of the asset space discovered by k-means, we develop a novel recommendation strategy for recommender systems. We present some encouraging results for two real world recommender systems. We conclude by outlining our approach to automatically generating descriptions of the clusters and report on an experiment designed to test concepts generated for the SmartRadio recommender system.en
dc.format.extent290441 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherTrinity College Dublin, Department of Computer Scienceen
dc.relation.ispartofseriesComputer Science Technical Reporten
dc.relation.ispartofseriesTCD-CS-2003-38en
dc.relation.haspartTCD-CS-[no.]en
dc.subjectComputer Scienceen
dc.titleConcept Discovery in Collaborative Recommender Systemsen
dc.typeTechnical Reporten
dc.identifier.rssurihttps://www.cs.tcd.ie/publications/tech-reports/reports.03/TCD-CS-2003-38.pdf


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