hopwise.model.sequential_recommender.gru4rec¶
- Reference:
Yong Kiam Tan et al. “Improved Recurrent Neural Networks for Session-based Recommendations.” in DLRS 2016.
Classes¶
GRU4Rec is a model that incorporate RNN for recommendation. |
Module Contents¶
- class hopwise.model.sequential_recommender.gru4rec.GRU4Rec(config, dataset)¶
Bases:
hopwise.model.abstract_recommender.SequentialRecommenderGRU4Rec is a model that incorporate RNN for recommendation.
Note
Regarding the innovation of this article,we can only achieve the data augmentation mentioned in the paper and directly output the embedding of the item, in order that the generation method we used is common to other sequential models.
- embedding_size¶
- loss_type¶
- num_layers¶
- dropout_prob¶
- item_embedding¶
- emb_dropout¶
- gru_layers¶
- dense¶
- _init_weights(module)¶
- forward(item_seq, item_seq_len)¶
- 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
- 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
- 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