Title: An empirical study of the recursive input generation algorithm for memory-based collaborative filtering recommender systems

Authors: Serhiy Morozov; Hossein Saiedian

Addresses: Mathematics, Computer Science, and Software Engineering, University of Detroit Mercy, Detroit, MI 48221-3038, USA ' Electrical Engineering and Computer Science, The University of Kansas, Lawrence, KS 66045, USA

Abstract: Recommender system research has gained popularity recently because many businesses are willing to pay for a way to predict future user opinions. Such knowledge could simplify decision-making, improve customer satisfaction, and increase sales. We focus on the recommendation accuracy of memory-based collaborative filtering recommender systems and propose a novel input generation algorithm that helps identify a small group of relevant ratings. Any combination algorithm can be used to generate a recommendation from such ratings. We attempt to improve the quality of these ratings through recursive sorting. Finally, we demonstrate the effectiveness of our approach on the Netflix dataset, a popular, large, and extremely sparse collection of movie ratings.

Keywords: recommender systems; recommendation accuracy; consumer behaviour; behaviour prediction; input generation algorithms; memory based collaborative filtering; recursive sorting; movie ratings; film ratings.

DOI: 10.1504/IJIDS.2013.052020

International Journal of Information and Decision Sciences, 2013 Vol.5 No.1, pp.36 - 49

Published online: 28 Feb 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article