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)

Bases: 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)

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)

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.

training: bool

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

Bases: SequentialRecommender

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 connection 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)

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)

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)

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)

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

training: bool