SLIMElastic

Reference:

Xia Ning et al. “SLIM: Sparse Linear Methods for Top-N Recommender Systems.” in ICDM 2011.

Reference code:

https://github.com/KarypisLab/SLIM https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation/blob/master/SLIM_ElasticNet/SLIMElasticNetRecommender.py

class recbole.model.general_recommender.slimelastic.SLIMElastic(config, dataset)

Bases: recbole.model.abstract_recommender.GeneralRecommender

SLIMElastic is a sparse linear method for top-K recommendation, which learns a sparse aggregation coefficient matrix by solving an L1-norm and L2-norm regularized optimization problem.

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()

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 = 1

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

type = 5