MacridVAE¶
- Reference:
Jianxin Ma et al. “Learning Disentangled Representations for Recommendation.” in NeurIPS 2019.
- Reference code:
- class recbole.model.general_recommender.macridvae.MacridVAE(config, dataset)[source]¶
Bases:
recbole.model.abstract_recommender.GeneralRecommender,recbole.model.abstract_recommender.AutoEncoderMixinMacridVAE 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)[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
- forward(rating_matrix)[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.
- 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
- input_type = 2¶
- 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
- reg_loss()[source]¶
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)
- training: bool¶