DiFacto: Distributed Factorization MachinesEI

    Cited by: 43|Bibtex|17|

    WSDM, pp. 377-386, 2016.

    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 ho...More
    Your rating :
    0

     

    Tags
    Comments