RippleNet¶
- Reference:
Hongwei Wang et al. “RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems.” in CIKM 2018.
- class recbole.model.knowledge_aware_recommender.ripplenet.RippleNet(config, dataset)[source]¶
Bases:
recbole.model.abstract_recommender.KnowledgeRecommenderRippleNet is an knowledge enhanced matrix factorization model. The original interaction matrix of \(n_{users} \times n_{items}\) and related knowledge graph is set as model input, we carefully design the data interface and use ripple set to train and test efficiently. We just implement the model following the original author with a pointwise training mode.
- 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(interaction)[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
- input_type = 1¶
- 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¶