MacridVAE

Reference:

Jianxin Ma et al. “Learning Disentangled Representations for Recommendation.” in NeurIPS 2019.

Reference code:

https://jianxinma.github.io/disentangle-recsys.html

class recbole.model.general_recommender.macridvae.MacridVAE(config, dataset)

Bases: GeneralRecommender, AutoEncoderMixin

MacridVAE is an item-based collaborative filtering model that learns disentangled representations from user behavior and simultaneously ranks all items for each user.

We implement the model following the original author.

calculate_loss(interaction)

Calculate the training loss for a batch data.

Parameters:

interaction (Interaction) – Interaction class of the batch.

Returns:

Training loss, shape: []

Return type:

torch.Tensor

forward(rating_matrix)

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 Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

full_sort_predict(interaction)

full sort prediction function. Given users, calculate the scores between users and all candidate items.

Parameters:

interaction (Interaction) – Interaction class of the batch.

Returns:

Predicted scores for given users and all candidate items, shape: [n_batch_users * n_candidate_items]

Return type:

torch.Tensor

input_type = 2

mlp_layers(layer_dims)

predict(interaction)

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

reg_loss()

Calculate the L2 normalization loss of model parameters. Including embedding matrices and weight matrices of model.

Returns:

The L2 Loss tensor. shape of [1,]

Return type:

loss(torch.FloatTensor)

reparameterize(mu, logvar)

training: bool