elliot.recommender.graph_based.lightgcn package

Submodules

elliot.recommender.graph_based.lightgcn.LightGCN module

Module description:

class elliot.recommender.graph_based.lightgcn.LightGCN.LightGCN(data, config, params, *args, **kwargs)

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

LightGCN: Simplifying and Powering Graph Convolution Network for 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

• l_w – Regularization coefficient

• n_layers – Number of embedding propagation layers

• n_fold – Number of folds to split the adjacency matrix into sub-matrices and ease the computation

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

models:
  LightGCN:
    meta:
      save_recs: True
    lr: 0.0005
    epochs: 50
    batch_size: 512
    factors: 64
    batch_size: 256
    l_w: 0.1
    n_layers: 1
    n_fold: 5

get_recommendations(k: int = 100)

property name

train()

elliot.recommender.graph_based.lightgcn.LightGCN_model module

Module description:

class elliot.recommender.graph_based.lightgcn.LightGCN_model.LightGCNModel(*args, **kwargs)

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

call(inputs, **kwargs)

Generates prediction for passed users and items indices

Parameters

• inputs – user, item (batch)

• Network in training mode or inference mode. (the) –

Returns

prediction and extracted model parameters

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, **kwargs)

train_step(batch)

Apply a single training step on one batch.

Parameters

batch – batch used for the current train step

Returns

loss value at the current batch

Module contents