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Title: Concept Discovery in Collaborative Recommender Systems
Author: Clerkin, Patrick
Cunningham, Pádraig
Hayes, Conor
Keywords: Computer Science
Issue Date: 6-Sep-2003
Publisher: Trinity College Dublin, Department of Computer Science
Citation: [Author surname, forename]. 'Concept Discovery in Collaborative Recommender Systems'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2003-38, 2003, pp6
Series/Report no.: Computer Science Technical Report
Abstract: 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.
Appears in Collections:Computer Science Technical Reports

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