elliot.recommender.latent_factor_models.BPRMF package

Submodules

elliot.recommender.latent_factor_models.BPRMF.BPRMF module

Module description:

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

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

Bayesian Personalized Ranking with Matrix Factorization

For further details, please refer to the paper

Parameters

• factors – Number of latent factors

• lr – Learning rate

• bias_regularization – Regularization coefficient for the bias

• user_regularization – Regularization coefficient for user latent factors

• positive_item_regularization – Regularization coefficient for positive item latent factors

• negative_item_regularization – Regularization coefficient for negative item latent factors

• update_negative_item_factors –

• update_users –

• update_items –

• update_bias –

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

models:
  BPRMF:
    meta:
      save_recs: True
    epochs: 10
    factors: 10
    lr: 0.001
    bias_regularization: 0
    user_regularization: 0.0025
    positive_item_regularization: 0.0025
    negative_item_regularization: 0.0025
    update_negative_item_factors: True
    update_users: True
    update_items: True
    update_bias: True

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()

train_step()

update_factors(u: int, i: int, j: int)

class elliot.recommender.latent_factor_models.BPRMF.BPRMF.MF(F, ratings, random, *args)

Bases: object

get_item_bias(item: int)

get_item_factors(item: int)

get_model_state()

get_transactions()

get_user_bias(user: int)

get_user_factors(user: int)

get_user_recs(user, k)

get_user_recs_argpartition(user: int, k: int)

initialize(loc: float = 0, scale: float = 0.1)

This function initialize the data model :param loc: :param scale: :return:

property name

predict(user, item)

set_item_bias(item: int, v: float)

set_item_factors(item: int, v: float)

set_model_state(saving_dict)

set_user_bias(user: int, v: float)

set_user_factors(user: int, v: float)

Module contents

Module description: