SpectralCF¶
- Reference:
Lei Zheng et al. “Spectral collaborative filtering.” in RecSys 2018.
- Reference code:
- class recbole.model.general_recommender.spectralcf.SpectralCF(config, dataset)[source]¶
Bases:
recbole.model.abstract_recommender.GeneralRecommenderSpectralCF is a spectral convolution model that directly learns latent factors of users and items from the spectral domain for recommendation.
The spectral convolution operation with C input channels and F filters is shown as the following:
\[\begin{split}\left[\begin{array} {c} X_{new}^{u} \\ X_{new}^{i} \end{array}\right]=\sigma\left(\left(U U^{\top}+U \Lambda U^{\top}\right) \left[\begin{array}{c} X^{u} \\ X^{i} \end{array}\right] \Theta^{\prime}\right)\end{split}\]where \(X_{new}^{u} \in R^{n_{users} \times F}\) and \(X_{new}^{i} \in R^{n_{items} \times F}\) denote convolution results learned with F filters from the spectral domain for users and items, respectively; \(\sigma\) denotes the logistic sigmoid function.
Note
Our implementation is a improved version which is different from the original paper. For a better stability, we replace \(U U^T\) with identity matrix \(I\) and replace \(U \Lambda U^T\) with laplace matrix \(L\).
- 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()[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
- get_ego_embeddings()[source]¶
Get the embedding of users and items and combine to an embedding matrix.
- Returns
Tensor of the embedding matrix. Shape of (n_items+n_users, embedding_dim)
- get_eye_mat(num)[source]¶
Construct the identity matrix with the size of n_items+n_users.
- Parameters
num – number of column of the square matrix
- Returns
Sparse tensor of the identity matrix. Shape of (n_items+n_users, n_items+n_users)
- get_laplacian_matrix()[source]¶
Get the laplacian matrix of users and items.
\[L = I - D^{-1} \times A\]- Returns
Sparse tensor of the laplacian matrix.
- input_type = 2¶
- 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¶