# SRGNN¶

Reference:

Shu Wu et al. “Session-based Recommendation with Graph Neural Networks.” in AAAI 2019.

Reference code:

https://github.com/CRIPAC-DIG/SR-GNN

class recbole.model.sequential_recommender.srgnn.GNN(embedding_size, step=1)[source]

Bases: torch.nn.modules.module.Module

Graph neural networks are well-suited for session-based recommendation, because it can automatically extract features of session graphs with considerations of rich node connections.

GNNCell(A, hidden)[source]

Obtain latent vectors of nodes via graph neural networks.

Parameters
• A (torch.FloatTensor) – The connection matrix,shape of [batch_size, max_session_len, 2 * max_session_len]

• hidden (torch.FloatTensor) – The item node embedding matrix, shape of [batch_size, max_session_len, embedding_size]

Returns

Latent vectors of nodes,shape of [batch_size, max_session_len, embedding_size]

Return type

torch.FloatTensor

forward(A, hidden)[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class recbole.model.sequential_recommender.srgnn.SRGNN(config, dataset)[source]

SRGNN regards the conversation history as a directed graph. In addition to considering the connection between the item and the adjacent item, it also considers the connection with other interactive items.

Such as: A example of a session sequence(eg:item1, item2, item3, item2, item4) and the connecion matrix A

Outgoing edges:

1

2

3

4

1

0

1

0

0

2

0

0

1/2

1/2

3

0

1

0

0

4

0

0

0

0

Incoming edges:

1

2

3

4

1

0

0

0

0

2

1/2

0

1/2

0

3

0

1

0

0

4

0

1

0

0

calculate_loss(interaction)[source]

Calculate the training loss for a batch data.

Parameters

interaction (Interaction) – Interaction class of the batch.

Returns

Training loss, shape: []

Return type

torch.Tensor

forward(item_seq, item_seq_len)[source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

full_sort_predict(interaction)[source]

full sort prediction function. Given users, calculate the scores between users and all candidate items.

Parameters

interaction (Interaction) – Interaction class of the batch.

Returns

Predicted scores for given users and all candidate items, shape: [n_batch_users * n_candidate_items]

Return type

torch.Tensor

predict(interaction)[source]

Predict the scores between users and items.

Parameters

interaction (Interaction) – Interaction class of the batch.

Returns

Predicted scores for given users and items, shape: [batch_size]

Return type

torch.Tensor