FISM¶

Reference:

Kabbur et al. “FISM: Factored item similarity models for top-n recommender systems” in KDD 2013

Reference code:

https://github.com/AaronHeee/Neural-Attentive-Item-Similarity-Model

class recbole.model.general_recommender.fism.FISM(config, dataset)[source]¶

Bases: recbole.model.abstract_recommender.GeneralRecommender

FISM is an item-based model for generating top-N recommendations that learns the item-item similarity matrix as the product of two low dimensional latent factor matrices. These matrices are learned using a structural equation modeling approach, where in the value being estimated is not used for its own estimation.

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)[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

get_history_info(dataset)[source]¶

get the user history interaction information

Parameters: dataset (DataSet) – train dataset
Returns: (history_item_matrix, history_lens, mask_mat)
Return type: tuple

input_type = 1¶

inter_forward(user, item)[source]¶: forward the model by interaction

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()[source]¶

calculate the reg loss for embedding layers

Returns: reg loss
Return type: torch.Tensor

training: bool¶

user_forward(user_input, item_num, user_bias, repeats=None, pred_slc=None)[source]¶

forward the model by user

Parameters

user_input (torch.Tensor) – user input tensor
item_num (torch.Tensor) – user history interaction lens
repeats (int, optional) – the number of items to be evaluated
pred_slc (torch.Tensor, optional) – continuous index which controls the current evaluation items, if pred_slc is None, it will evaluate all items

Returns

result

Return type

torch.Tensor