Personalised recommendations in e-commerce Online publication date: Wed, 02-Mar-2005
by Krishnamoorthy Srikumar, Bharat Bhasker
International Journal of Electronic Business (IJEB), Vol. 3, No. 1, 2005
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Electronic Business (IJEB):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com