elliot.recommender.latent_factor_models.SVDpp package¶
Submodules¶
elliot.recommender.latent_factor_models.SVDpp.svdpp module¶
Module description:
-
class
elliot.recommender.latent_factor_models.SVDpp.svdpp.
SVDpp
(data, config, params, *args, **kwargs)[source]¶ Bases:
elliot.recommender.recommender_utils_mixin.RecMixin
,elliot.recommender.base_recommender_model.BaseRecommenderModel
SVD++
For further details, please refer to the paper
- Parameters
factors – Number of latent factors
lr – Learning rate
reg_w – Regularization coefficient for latent factors
reg_b – Regularization coefficient for bias
To include the recommendation model, add it to the config file adopting the following pattern:
models: SVDpp: meta: save_recs: True epochs: 10 batch_size: 512 factors: 50 lr: 0.001 reg_w: 0.1 reg_b: 0.001
-
property
name
¶
elliot.recommender.latent_factor_models.SVDpp.svdpp_model module¶
Module description:
-
class
elliot.recommender.latent_factor_models.SVDpp.svdpp_model.
SVDppModel
(*args, **kwargs)[source]¶ Bases:
tensorflow.python.keras.engine.training.Model
-
get_recs
(inputs, training=False, **kwargs)[source]¶ Get full predictions on the whole users/items matrix.
- Returns
The matrix of predicted values.
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