elliot.recommender.latent_factor_models.NonNegMF package

Submodules

elliot.recommender.latent_factor_models.NonNegMF.non_negative_matrix_factorization module

Module description:

class elliot.recommender.latent_factor_models.NonNegMF.non_negative_matrix_factorization.NonNegMF(data, config, params, *args, **kwargs)

Bases: elliot.recommender.recommender_utils_mixin.RecMixin, elliot.recommender.base_recommender_model.BaseRecommenderModel

Non-Negative Matrix Factorization

For further details, please refer to the paper

Parameters

• factors – Number of latent factors

• lr – Learning rate

• reg – Regularization coefficient

To include the recommendation model, add it to the config file adopting the following pattern:

models:
  NonNegMF:
    meta:
      save_recs: True
    epochs: 10
    batch_size: 512
    factors: 10
    lr: 0.001
    reg: 0.1

get_recommendations(k: int = 10)

get_single_recommendation(mask, k, *args)

property name

train()

elliot.recommender.latent_factor_models.NonNegMF.non_negative_matrix_factorization_model module

Module description:

class elliot.recommender.latent_factor_models.NonNegMF.non_negative_matrix_factorization_model.NonNegMFModel(data, num_users, num_items, global_mean, embed_mf_size, lambda_weights, learning_rate=0.01, random_seed=42)

Bases: object

get_model_state()

get_user_recs(user, mask, k=100)

load_weights(path)

predict(user, item)

save_weights(path)

set_model_state(saving_dict)

train_step()

Module contents

Module description: