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)

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

get_recommendations(k: int = 100)

property name

train()

elliot.recommender.visual_recommenders.VBPR.VBPR_model module

Module description:

class elliot.recommender.visual_recommenders.VBPR.VBPR_model.VBPRModel(*args, **kwargs)

Bases: tensorflow.python.keras.engine.training.Model

call(inputs, training=None)

get_config()

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.

get_top_k(preds, train_mask, k=100)

predict(start, stop)

predict_item_batch(start, stop, start_item, stop_item, feat)

train_step(batch)

Module contents

Module description: