PREA: Personalized Recommendation Algorithms Toolkit
Joonseok Lee, Mingxuan Sun, Guy Lebanon; 13(87):2699−2703, 2012.
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.
[abs]
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