DiffRec¶

Reference:: Wenjie Wang et al. “Diffusion Recommender Model.” in SIGIR 2023.
Reference code:: https://github.com/YiyanXu/DiffRec

class recbole.model.general_recommender.ldiffrec.AutoEncoder(item_emb, n_cate, in_dims, out_dims, device, act_func, reparam=True, dropout=0.1)[source]¶

Bases: torch.nn.modules.module.Module

Guassian Diffusion for large-scale recommendation.

Decode(batch)[source]¶

Encode(batch)[source]¶

reparamterization(mu, logvar)[source]¶

training: bool¶

class recbole.model.general_recommender.ldiffrec.LDiffRec(config, dataset)[source]¶

Bases: recbole.model.general_recommender.diffrec.DiffRec

L-DiffRec clusters items into groups, compresses the interaction vector over each group into a low-dimensional latent vector via a group-specific VAE, and conducts the forward and reverse diffusion processes in the latent space.

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

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¶

recbole.model.general_recommender.ldiffrec.compute_loss(recon_x, x)[source]¶