Caser¶
- Reference:
Jiaxi Tang et al., “Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding” in WSDM 2018.
- Reference code:
- class recbole.model.sequential_recommender.caser.Caser(config, dataset)[source]¶
Bases:
recbole.model.abstract_recommender.SequentialRecommenderCaser 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. In addition, to prevent excessive CNN layers (ValueError: Training loss is nan), please make sure the parameters MAX_ITEM_LIST_LENGTH small, such as 10.
- 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
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
- 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¶