PREA: personalized recommendation algorithms toolkit

Authors:
Joonseok Lee;Mingxuan Sun;Guy Lebanon
Affiliations:
College of Computing, Georgia Institute of Technology, Atlanta, Georgia;College of Computing, Georgia Institute of Technology, Atlanta, Georgia;College of Computing, Georgia Institute of Technology, Atlanta, Georgia
Venue:
The Journal of Machine Learning Research
Year:
2012

Citing 7
Cited 1

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Non-linear matrix factorization with Gaussian processes

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Fast nonparametric matrix factorization for large-scale collaborative filtering

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A survey of collaborative filtering techniques

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A Survey of Accuracy Evaluation Metrics of Recommendation Tasks

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Empirical analysis of predictive algorithms for collaborative filtering

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Local collaborative ranking

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Abstract

Recommendation systems are important business applications with significant economic impact. In recent years, a large number of algorithms have been proposed for recommendation systems. In this paper, we describe an open-source toolkit implementing many recommendation algorithms as well as popular evaluation metrics. In contrast to other packages, our toolkit implements recent state-of-the-art algorithms as well as most classic algorithms.