elliot.recommender.latent_factor_models.Slim package

Submodules

elliot.recommender.latent_factor_models.Slim.slim module

Module description:

class elliot.recommender.latent_factor_models.Slim.slim.Slim(data, config, params, *args, **kwargs)

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

Train a Sparse Linear Methods (SLIM) item similarity model.

NOTE: ElasticNet solver is parallel, a single intance of SLIM_ElasticNet will

make use of half the cores available

See:

Efficient Top-N Recommendation by Linear Regression, M. Levy and K. Jack, LSRS workshop at RecSys 2013.

SLIM: Sparse linear methods for top-n recommender systems, X. Ning and G. Karypis, ICDM 2011. For further details, please refer to the paper

Parameters

• l1_ratio –

• alpha –

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

models:
  Slim:
    meta:
      save_recs: True
    l1_ratio: 0.001
    alpha: 0.001

get_recommendations(k: int = 10)

get_single_recommendation(mask, k, *args)

property name

predict(u: int, i: int)

Get prediction on the user item pair.

Returns

A single float vaue.

train()

elliot.recommender.latent_factor_models.Slim.slim_model module

Module description:

class elliot.recommender.latent_factor_models.Slim.slim_model.SlimModel(data, num_users, num_items, l1_ratio, alpha, epochs, neighborhood, random_seed)

Bases: object

get_model_state()

get_user_recs(user, mask, k=100)

load_weights(path)

predict(u, i)

prepare_predictions()

save_weights(path)

set_model_state(saving_dict)

train(verbose)

Module contents

Module description: