elliot.recommender.latent_factor_models.BPRSlim package

Submodules

elliot.recommender.latent_factor_models.BPRSlim.bprslim module

Module description:

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

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

BPR Sparse Linear Methods

For further details, please refer to the paper

Parameters

• factors – Number of latent factors

• lr – Learning rate

• lj_reg – Regularization coefficient for positive items

• li_reg – Regularization coefficient for negative items

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

models:
  AMF:
    meta:
      save_recs: True
    epochs: 10
    batch_size: 512
    factors: 10
    lr: 0.001
    lj_reg: 0.001
    li_reg: 0.1

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.

restore_weights()

train()

elliot.recommender.latent_factor_models.BPRSlim.bprslim_model module

Module description:

class elliot.recommender.latent_factor_models.BPRSlim.bprslim_model.BPRSlimModel(data, num_users, num_items, lr, lj_reg, li_reg, sampler, random_seed=42)

Bases: object

get_model_state()

get_user_recs(user, mask, k=100)

load_weights(path)

predict(u, i)

save_weights(path)

set_model_state(saving_dict)

train_step(batch)

Module contents

Module description: