hopwise.model.context_aware_recommender.deepfm¶

Reference:: Huifeng Guo et al. “DeepFM: A Factorization-Machine based Neural Network for CTR Prediction.” in IJCAI 2017.

Classes¶

DeepFM is a DNN enhanced FM which both use a DNN and a FM to calculate feature interaction.

Module Contents¶

class hopwise.model.context_aware_recommender.deepfm.DeepFM(config, dataset)[source]¶

Bases: hopwise.model.abstract_recommender.ContextRecommender

DeepFM is a DNN enhanced FM which both use a DNN and a FM to calculate feature interaction. Also DeepFM can be seen as a combination of FNN and FM.

mlp_hidden_size¶

dropout_prob¶

fm¶

mlp_layers¶

deep_predict_layer¶

sigmoid¶

loss¶

_init_weights(module)[source]¶

forward(interaction)[source]¶

calculate_loss(interaction)[source]¶

Calculate the training loss for a batch data.

Parameters:: interaction (Interaction) – Interaction class of the batch.
Returns:: Training loss, shape: []
Return type:: torch.Tensor

predict(interaction)[source]¶

Predict the scores between users and items.

Parameters:: interaction (Interaction) – Interaction class of the batch.
Returns:: Predicted scores for given users and items, shape: [batch_size]
Return type:: torch.Tensor