Authors: Krishnamoorthy Srikumar, Bharat Bhasker
Addresses: Indian Institute of Management, Off Sitapur Road, Lucknow 226 013, India. ' Indian Institute of Management, Off Sitapur Road, Lucknow 226 013, India
Abstract: Most of the current personalised recommender systems use either collaborative filtering or data mining for offering recommendations. However, such methods are beset with problems of sparsity and scalability. In this paper, we present a System for Personalised REcommendations in E-commerce (SPREE) that combines the strengths of both collaborative filtering and data mining for providing better recommendations. We experimentally evaluate our system and show the benefits using a set of real and synthetic datasets. We also propose a novel similarity metric for efficiently computing collaborative users. Experimental results show that the proposed similarity metric is up to 12 orders of magnitude faster and has better predictive capabilities compared to other similarity metrics.
Keywords: recommender systems; collaborative filtering; data mining; e-commerce; electronic commerce; personalised recommendations; customisation.
International Journal of Electronic Business, 2005 Vol.3 No.1, pp.4 - 27
Published online: 02 Mar 2005 *Full-text access for editors Access for subscribers Purchase this article Comment on this article