HRM¶

Reference:: Pengfei Wang et al. “Learning Hierarchical Representation Model for Next Basket Recommendation.” in SIGIR 2015.
Reference code:: https://github.com/wubinzzu/NeuRec

class recbole.model.sequential_recommender.hrm.HRM(config, dataset)[source]¶

Bases: recbole.model.abstract_recommender.SequentialRecommender

HRM can well capture both sequential behavior and users’ general taste by involving transaction and user representations in prediction.

HRM user max- & average- pooling as a good helper.

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(seq_item, user, seq_item_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

inverse_seq_item(seq_item, seq_item_len)[source]¶

inverse the seq_item, like this: [1,2,3,0,0,0,0] – after inverse –>> [0,0,0,0,1,2,3]

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

training: bool¶