KSR¶

Reference:: Jin Huang et al. “Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks.” In SIGIR 2018

class recbole.model.sequential_recommender.ksr.KSR(config, dataset)[source]¶

Bases: recbole.model.abstract_recommender.SequentialRecommender

KSR integrates the RNN-based networks with Key-Value Memory Network (KV-MN). And it further incorporates knowledge base (KB) information to enhance the semantic representation of KV-MN.

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 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)[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

memory_read(seq_output, user_memory)[source]¶: define read operator

memory_update(item_seq, item_seq_len)[source]¶: define write operator

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¶