hopwise.model.general_recommender.multivae¶

Reference:: Dawen Liang et al. “Variational Autoencoders for Collaborative Filtering.” in WWW 2018.

Classes¶

MultiVAE is an item-based collaborative filtering model that simultaneously ranks all items for each user.

Module Contents¶

class hopwise.model.general_recommender.multivae.MultiVAE(config, dataset)[source]¶

Bases: hopwise.model.abstract_recommender.GeneralRecommender, hopwise.model.abstract_recommender.AutoEncoderMixin

MultiVAE is an item-based collaborative filtering model that simultaneously ranks all items for each user.

We implement the MultiVAE model with only user dataloader.

input_type¶

layers¶

lat_dim¶

drop_out¶

anneal_cap¶

total_anneal_steps¶

update = 0¶

encode_layer_dims¶

decode_layer_dims¶

encoder¶

decoder¶

mlp_layers(layer_dims)[source]¶

reparameterize(mu, logvar)[source]¶

forward(rating_matrix)[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

full_sort_predict(interaction)[source]¶

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