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:
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¶
- 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