elliot.recommender.latent_factor_models.WRMF package

Submodules

elliot.recommender.latent_factor_models.WRMF.wrmf module

Module description:

class elliot.recommender.latent_factor_models.WRMF.wrmf.WRMF(data, config, params, *args, **kwargs)

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

Weighted XXX Matrix Factorization

For further details, please refer to the paper

Parameters

• factors – Number of latent factors

• lr – Learning rate

• alpha –

• reg – Regularization coefficient

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

models:
  WRMF:
    meta:
      save_recs: True
    epochs: 10
    factors: 50
    alpha: 1
    reg: 0.1

get_recommendations(k: int = 100)

property name

predict(u: int, i: int)

Get prediction on the user item pair.

Returns

A single float vaue.

restore_weights()

train()

elliot.recommender.latent_factor_models.WRMF.wrmf_model module

Module description:

class elliot.recommender.latent_factor_models.WRMF.wrmf_model.WRMFModel(factors, data, random, alpha, reg)

Bases: object

Simple Matrix Factorization class

get_model_state()

get_user_recs(user, k=100)

predict(user, item)

set_model_state(saving_dict)

train_step()

Module contents