FwFM¶
- Reference:
Junwei Pan et al. “Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising.” in WWW 2018.
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class
recbole.model.context_aware_recommender.fwfm.FwFM(config, dataset)[source]¶ Bases:
recbole.model.abstract_recommender.ContextRecommenderFwFM is a context-based recommendation model. It aims to model the different feature interactions between different fields in a much more memory-efficient way. It proposes a field pair weight matrix \(r_{F(i),F(j)}\), to capture the heterogeneity of field pair interactions.
The model defines as follows:
\[y = w_0 + \sum_{i=1}^{m}x_{i}w_{i} + \sum_{i=1}^{m}\sum_{j=i+1}^{m}x_{i}x_{j}<v_{i}, v_{j}>r_{F(i),F(j)}\]-
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
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forward(interaction)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
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fwfm_layer(infeature)[source]¶ Get the field pair weight matrix r_{F(i),F(j)}, and model the different interaction strengths of different field pairs \(\sum_{i=1}^{m}\sum_{j=i+1}^{m}x_{i}x_{j}<v_{i}, v_{j}>r_{F(i),F(j)}\).
- Parameters
infeature (torch.cuda.FloatTensor) – [batch_size, field_size, embed_dim]
- Returns
[batch_size, 1]
- Return type
torch.cuda.FloatTensor
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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
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