FPMC

Reference:

Steffen Rendle et al. “Factorizing Personalized Markov Chains for Next-Basket Recommendation.” in WWW 2010.

class recbole.model.sequential_recommender.fpmc.FPMC(config, dataset)

Bases: recbole.model.abstract_recommender.SequentialRecommender

The FPMC model is mainly used in the recommendation system to predict the possibility of unknown items arousing user interest, and to discharge the item recommendation list.

Note

In order that the generation method we used is common to other sequential models, We set the size of the basket mentioned in the paper equal to 1. For comparison with other models, the loss function used is BPR.

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

forward(user, item_seq, item_seq_len, next_item)

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)

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

input_type = 2

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

training: bool