elliot.recommender.visual_recommenders.DeepStyle package

Submodules

elliot.recommender.visual_recommenders.DeepStyle.DeepStyle module

Module description:

class elliot.recommender.visual_recommenders.DeepStyle.DeepStyle.DeepStyle(data, config, params, *args, **kwargs)

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

DeepStyle: Learning User Preferences for Visual Recommendation

For further details, please refer to the paper

Parameters

• lr – Learning rate

• epochs – Number of epochs

• factors – Number of latent factors

• batch_size – Batch size

• batch_eval – Batch size for evaluation

• l_w – Regularization coefficient

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

models:
  DeepStyle:
    meta:
      save_recs: True
    lr: 0.0005
    epochs: 50
    factors: 100
    batch_size: 128
    batch_eval: 512
    l_w: 0.000025

get_recommendations(k: int = 100)

property name

train()

elliot.recommender.visual_recommenders.DeepStyle.DeepStyle_model module

Module description:

class elliot.recommender.visual_recommenders.DeepStyle.DeepStyle_model.DeepStyleModel(*args, **kwargs)

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

call(inputs, training=None)

get_config()

get_top_k(preds, train_mask, k=100)

predict_batch(start, stop, gi, li, fi)

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

train_step(batch)

Module contents

Module description: