PREA: personalized recommendation algorithms toolkit. 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.

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References in zbMATH (referenced in 2 articles , 1 standard article )

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  1. Lee, Joonseok; Kim, Seungyeon; Lebanon, Guy; Singer, Yoram; Bengio, Samy: LLORMA: local low-rank matrix approximation (2016)
  2. Lee, Joonseok; Sun, Mingxuan; Lebanon, Guy: PREA: personalized recommendation algorithms toolkit (2012) ioport