Trinity College Dublin, Department of Computer Science
[Author surname, forename]. 'Concept Discovery in Collaborative Recommender Systems'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2003-38, 2003, pp6
Computer Science Technical Report 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.
Please note: There is a known bug in some browsers that causes an
error when a user tries to view large pdf file within the browser window.
If you receive the message "The file is damaged and could not be
repaired", please try one of the solutions linked below based on the
browser you are using.
Items in TARA are protected by copyright, with all rights reserved, unless otherwise indicated.