Graph-based¶

Elliot integrates, to date, 50 recommendation models partitioned into two sets. The first set includes 38 popular models implemented in at least two of frameworks reviewed in this work (i.e., adopting a framework-wise popularity notion).

Summary¶

`lightgcn.LightGCN.LightGCN`(data, config, …)	LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
`ngcf.NGCF.NGCF`(data, config, params, *args, …)	Neural Graph Collaborative Filtering

LightGCN¶

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

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
    factors: 64
    batch_size: 256
    l_w: 0.1
    n_layers: 1
    n_fold: 5

NGCF¶

class elliot.recommender.graph_based.ngcf.NGCF.NGCF(data, config, params, *args, **kwargs)[source]¶

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

Neural Graph Collaborative Filtering

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
weight_size – Tuple with number of units for each embedding propagation layer
node_dropout – Tuple with dropout rate for each node
message_dropout – Tuple with dropout rate for each embedding propagation layer
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:
  NGCF:
    meta:
      save_recs: True
    lr: 0.0005
    epochs: 50
    factors: 64
    batch_size: 256
    l_w: 0.1
    weight_size: (64,)
    node_dropout: ()
    message_dropout: (0.1,)
    n_fold: 5