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Fast nonparametric matrix factorization for large-scale collaborative filtering. The 32nd SIGIR conference (2009)
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@MISC{Yu09fastnonparametric,
author = {Kai Yu and Shenghuo Zhu and John Lafferty and Yihong Gong},
title = {Fast nonparametric matrix factorization for large-scale collaborative filtering. The 32nd SIGIR conference},
year = {2009}
}
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Abstract
With the sheer growth of online user data, it becomes challenging to develop preference learning algorithms that are sufficiently flexible in modeling but also affordable in computation. In this paper we develop nonparametric matrix factorization methods by allowing the latent factors of two low-rank matrix factorization methods, the singular value decomposition (SVD) and probabilistic principal component analysis (pPCA), to be data-driven, with the dimensionality increasing with data size. We show that the formulations of the two nonparametric models are very similar, and their optimizations share similar procedures. Compared to traditional parametric low-rank methods, nonparametric models are appealing for their flexibility in modeling complex data dependencies. However, this modeling advantage comes at a computational price — it is highly challenging to scale them to large-scale problems, hampering their application to applications such as collaborative filtering. In this paper we introduce novel optimization algorithms, which are simple to implement, which allow learning both nonparametric matrix factorization models to be highly efficient on large-scale problems. Our experiments on EachMovie and Netflix, the two largest public benchmarks to date, demonstrate that the nonparametric models make more accurate predictions of user ratings, and are computationally comparable or sometimes even faster in training, in comparison with previous state-of-the-art parametric matrix factorization models.
Keyphrases
large-scale collaborative filtering nonparametric model sigir conference fast nonparametric matrix factorization large-scale problem modeling advantage singular value decomposition traditional parametric low-rank method sheer growth collaborative filtering low-rank matrix factorization method public benchmark complex data dependency user rating accurate prediction data size nonparametric matrix factorization method computational price latent factor nonparametric matrix factorization model optimization share similar procedure novel optimization algorithm online user data probabilistic principal component analysis
