SRGNN¶
- Reference:
Shu Wu et al. “Session-based Recommendation with Graph Neural Networks.” in AAAI 2019.
- Reference code:
- class recbole.model.sequential_recommender.srgnn.GNN(embedding_size, step=1)[source]¶
Bases:
torch.nn.modules.module.ModuleGraph 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
Moduleinstance 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)[source]¶
Bases:
recbole.model.abstract_recommender.SequentialRecommenderSRGNN 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)[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
Moduleinstance 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
- training: bool¶