GRU4Rec¶

Reference:: Yong Kiam Tan et al. “Improved Recurrent Neural Networks for Session-based Recommendations.” in DLRS 2016.

class recbole.model.sequential_recommender.gru4rec.GRU4Rec(config, dataset)[source]¶

Bases: recbole.model.abstract_recommender.SequentialRecommender

GRU4Rec 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.

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(item_seq, item_seq_len)[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 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)[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

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