# torchfm.model

## torchfm.model.afi

class `torchfm.model.afi.``AutomaticFeatureInteractionModel`(field_dims, embed_dim, num_heads, num_layers, mlp_dims, dropouts)

  A pytorch implementation of AutoInt.

  Reference:

    W Song, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks, 2018.

  `forward`(x)
    Parameters

      x – Long tensor of size `(batch_size, num_fields)`

## torchfm.model.afm

class `torchfm.model.afm.``AttentionalFactorizationMachineModel`(field_dims, embed_dim, attn_size, dropouts)

  A pytorch implementation of Attentional Factorization Machine.

  Reference:

    J Xiao, et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks, 2017.

  `forward`(x)
    Parameters

      x – Long tensor of size `(batch_size, num_fields)`

## torchfm.model.dcn

class `torchfm.model.dcn.``DeepCrossNetworkModel`(field_dims, embed_dim, num_layers, mlp_dims, dropout)

  A pytorch implementation of Deep & Cross Network.

  Reference:

    R Wang, et al. Deep & Cross Network for Ad Click Predictions, 2017.

  `forward`(x)
    Parameters

      x – Long tensor of size `(batch_size, num_fields)`

## torchfm.model.dfm

class `torchfm.model.dfm.``DeepFactorizationMachineModel`(field_dims, embed_dim, mlp_dims, dropout)

  A pytorch implementation of DeepFM.

  Reference:

    H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, 2017.

  `forward`(x)
    Parameters

      x – Long tensor of size `(batch_size, num_fields)`

## torchfm.model.ffm

class `torchfm.model.ffm.``FieldAwareFactorizationMachineModel`(field_dims, embed_dim)

  A pytorch implementation of Field-aware Factorization Machine.

  Reference:

    Y Juan, et al. Field-aware Factorization Machines for CTR Prediction, 2015.

  `forward`(x)
    Parameters

      x – Long tensor of size `(batch_size, num_fields)`

## torchfm.model.fm

class `torchfm.model.fm.``FactorizationMachineModel`(field_dims, embed_dim)

  A pytorch implementation of Factorization Machine.

  Reference:

    S Rendle, Factorization Machines, 2010.

  `forward`(x)
    Parameters

      x – Long tensor of size `(batch_size, num_fields)`

## torchfm.model.fnfm

class `torchfm.model.fnfm.``FieldAwareNeuralFactorizationMachineModel`(field_dims, embed_dim, mlp_dims, dropouts)

  A pytorch implementation of Field-aware Neural Factorization Machine.

  Reference:

    L Zhang, et al. Field-aware Neural Factorization Machine for Click-Through Rate Prediction, 2019.

  `forward`(x)
    Parameters

      x – Long tensor of size `(batch_size, num_fields)`

## torchfm.model.fnn

class `torchfm.model.fnn.``FactorizationSupportedNeuralNetworkModel`(field_dims, embed_dim, mlp_dims, dropout)

  A pytorch implementation of Neural Factorization Machine.

  Reference:

    W Zhang, et al. Deep Learning over Multi-field Categorical Data - A Case Study on User Response Prediction, 2016.

  `forward`(x)
    Parameters

      x – Long tensor of size `(batch_size, num_fields)`

## torchfm.model.lr

class `torchfm.model.lr.``LogisticRegressionModel`(field_dims)

  A pytorch implementation of Logistic Regression.

  `forward`(x)
    Parameters

      x – Long tensor of size `(batch_size, num_fields)`

## torchfm.model.nfm

class `torchfm.model.nfm.``NeuralFactorizationMachineModel`(field_dims, embed_dim, mlp_dims, dropouts)

  A pytorch implementation of Neural Factorization Machine.

  Reference:

    X He and TS Chua, Neural Factorization Machines for Sparse Predictive Analytics, 2017.

  `forward`(x)
    Parameters

      x – Long tensor of size `(batch_size, num_fields)`

## torchfm.model.pnn

class `torchfm.model.pnn.``ProductNeuralNetworkModel`(field_dims, embed_dim, mlp_dims, dropout, method='inner')

  A pytorch implementation of inner/outer Product Neural Network.

  Reference:

    Y Qu, et al. Product-based Neural Networks for User Response Prediction, 2016.

  `forward`(x)
    Parameters

      x – Long tensor of size `(batch_size, num_fields)`

## torchfm.model.wd

class `torchfm.model.wd.``WideAndDeepModel`(field_dims, embed_dim, mlp_dims, dropout)

  A pytorch implementation of wide and deep learning.

  Reference:

    HT Cheng, et al. Wide & Deep Learning for Recommender Systems, 2016.

  `forward`(x)
    Parameters

      x – Long tensor of size `(batch_size, num_fields)`

## torchfm.model.xdfm

class `torchfm.model.xdfm.``ExtremeDeepFactorizationMachineModel`(field_dims, embed_dim, mlp_dims, dropout, cross_layer_sizes, split_half=True)

  A pytorch implementation of xDeepFM.

  Reference:

    J Lian, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems, 2018.

  `forward`(x)
    Parameters

      x – Long tensor of size `(batch_size, num_fields)`