elliot.recommender.visual_recommenders.VBPR package¶
Submodules¶
elliot.recommender.visual_recommenders.VBPR.VBPR module¶
Module description:
-
class
elliot.recommender.visual_recommenders.VBPR.VBPR.
VBPR
(data, config, params, *args, **kwargs)[source]¶ Bases:
elliot.recommender.recommender_utils_mixin.RecMixin
,elliot.recommender.base_recommender_model.BaseRecommenderModel
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
For further details, please refer to the paper
- Parameters
lr – Learning rate
epochs – Number of epochs
factors – Number of latent factors
factors_d – Dimension of visual factors
batch_size – Batch size
batch_eval – Batch for evaluation
l_w – Regularization coefficient
l_b – Regularization coefficient of bias
l_e – Regularization coefficient of projection matrix
To include the recommendation model, add it to the config file adopting the following pattern:
models: VBPR: meta: save_recs: True lr: 0.0005 epochs: 50 factors: 100 factors_d: 20 batch_size: 128 batch_eval: 128 l_w: 0.000025 l_b: 0 l_e: 0.002
-
property
name
¶
elliot.recommender.visual_recommenders.VBPR.VBPR_model module¶
Module description:
-
class
elliot.recommender.visual_recommenders.VBPR.VBPR_model.
VBPRModel
(*args, **kwargs)[source]¶ Bases:
tensorflow.python.keras.engine.training.Model
-
get_config
()[source]¶ Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.
The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).
- Returns
Python dictionary.
-
Module contents¶
Module description: