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

An empirical study of the recursive input generation algorithm for memory-based collaborative... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Information and Decision Sciences Inderscience Publishers

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

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1756-7017
eISSN
1756-7025
DOI
10.1504/IJIDS.2013.052020
Publisher site
See Article on Publisher Site

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.

Journal

International Journal of Information and Decision SciencesInderscience Publishers

Published: Jan 1, 2013

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