# 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]

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(user_memory)[source]

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