Caser¶

Reference:: Jiaxi Tang et al., “Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding” in WSDM 2018.
Reference code:: https://github.com/graytowne/caser_pytorch

class recbole.model.sequential_recommender.caser.Caser(config, dataset)[source]¶

Bases: recbole.model.abstract_recommender.SequentialRecommender

Caser is a model that incorporate CNN for recommendation.

Note

We did not use the sliding window to generate training instances as in the paper, in order that the generation method we used is common to other sequential models. For comparison with other models, we set the parameter T in the paper as 1.

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(user, item_seq)[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

reg_loss_conv_h()[source]¶: L2 loss on conv_h