elliot.recommender.latent_factor_models.PureSVD package

Submodules

elliot.recommender.latent_factor_models.PureSVD.pure_svd module

Module description:

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

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

For further details, please refer to the paper

Parameters

• factors – Number of latent factors

• seed – Random seed

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

models:
  PureSVD:
    meta:
      save_recs: True
    factors: 10
    seed: 42

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.PureSVD.pure_svd_model module

Module description:

class elliot.recommender.latent_factor_models.PureSVD.pure_svd_model.PureSVDModel(factors, data, random_seed)

Bases: object

Simple Matrix Factorization class

get_model_state()

get_user_recs(user_id, mask, top_k=100)

load_weights(path)

predict(user, item)

save_weights(path)

set_model_state(saving_dict)

train_step()

Module contents