Collaborative recommender systems: Combining effectiveness and efficiency

Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative filtering (CF) is a successful recommendation technique that confronts the “information overload” problem. Memory-based algorithms recommend according to the prefere...

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Bibliographic Details
Published in:Expert systems with applications Vol. 34; no. 4; pp. 2995 - 3013
Main Authors: Symeonidis, Panagiotis, Nanopoulos, Alexandros, Papadopoulos, Apostolos N., Manolopoulos, Yannis
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.05.2008
Subjects:
Collaborative filtering
Nearest neighbors
Recommender system
Collaborative filtering
Nearest neighbors
Recommender system
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative filtering (CF) is a successful recommendation technique that confronts the “information overload” problem. Memory-based algorithms recommend according to the preferences of nearest neighbors, and model-based algorithms recommend by first developing a model of user ratings. In this paper, we bring to surface factors that affect CF process in order to identify existing false beliefs. In terms of accuracy, by being able to view the “big picture”, we propose new approaches that substantially improve the performance of CF algorithms. For instance, we obtain more than 40% increase in precision in comparison to widely-used CF algorithms. In terms of efficiency, we propose a model-based approach based on latent semantic indexing (LSI), that reduces execution times at least 50% than the classic CF algorithms.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2007.05.013