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Concept Discovery in Collaborative Recommender Systems |
Clerkin, Patrick; Cunningham, Padraig; Hayes, Conor
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TCD-CS-2003-38 There 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.
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Keyword(s):
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Computer Science |
Publication Date:
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2003 |
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Type:
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Report |
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Peer-Reviewed:
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Unknown |
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Language(s):
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English |
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Institution:
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Trinity College Dublin |
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Citation(s):
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[Author surname, forename]. 'Concept Discovery in Collaborative Recommender Systems'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2003-38, 2003, pp6 |
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Publisher(s):
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Trinity College Dublin, Department of Computer Science |
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File Format(s):
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application/pdf |
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First Indexed:
2014-05-13 05:32:32 Last Updated:
2015-04-10 05:14:24 |
