Concept Discovery in Collaborative Recommender Systems

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.