CKE

Reference:

Fuzheng Zhang et al. “Collaborative Knowledge Base Embedding for Recommender Systems.” in SIGKDD 2016.

class recbole.model.knowledge_aware_recommender.cke.CKE(config, dataset)

Bases: recbole.model.abstract_recommender.KnowledgeRecommender

CKE is a knowledge-based recommendation model, it can incorporate KG and other information such as corresponding images to enrich the representation of items for item recommendations.

Note

In the original paper, CKE used structural knowledge, textual knowledge and visual knowledge. In our implementation, we only used structural knowledge. Meanwhile, the version we implemented uses a simpler regular way which can get almost the same result (even better) as the original regular way.

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(user, item)

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

input_type = 2

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