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- W2255575265 abstract "Factorization Machines offer good performance and useful embeddings of data. However, they are costly to scale to large amounts of data and large numbers of features. In this paper we describe DiFacto, which uses a refined Factorization Machine model with sparse memory adaptive constraints and frequency adaptive regularization. We show how to distribute DiFacto over multiple machines using the Parameter Server framework by computing distributed subgradients on minibatches asynchronously. We analyze its convergence and demonstrate its efficiency in computational advertising datasets with billions examples and features." @default.
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- W2255575265 date "2016-02-08" @default.
- W2255575265 modified "2023-09-30" @default.
- W2255575265 title "DiFacto" @default.
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- W2255575265 doi "https://doi.org/10.1145/2835776.2835781" @default.
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