Authors: Subhash K. Shinde, Uday V. Kulkarni
Addresses: Bharati Vidyapeeth College of Engineering, Sector-07, C.B.D. Belpada, Navi Mumbai, 400 614, India. ' Bharati Vidyapeeth College of Engineering, Sector-07, C.B.D. Belpada, Navi Mumbai, 400 614, India
Abstract: Recommendation systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become important applications in electronic commerce for information access and for providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content, collaborative and knowledge-based techniques. However, there remain many challenges in deploying traditional recommendation techniques for e-commerce. This paper addresses these key challenges and proposes new techniques that combine the content and collaborative-based filtering to capitalise on their respective strengths and thereby achieve better performance. We describe new architecture for hybrid recommendation system. The results obtained empirically demonstrate that the proposed recommendation algorithms perform better and alleviate the challenges such as data sparsity and scalability.
Keywords: collaborative filtering; hybrid filtering; personalised recommendation systems; web recommendation systems; web usage mining; internet; e-commerce; electronic commerce; recommender systems; data sparsity; scalability; personalisation.
International Journal of Data Mining, Modelling and Management, 2010 Vol.2 No.4, pp.315 - 333
Published online: 30 Sep 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article