# TransRec¶

Reference:

Ruining He et al. “Translation-based Recommendation.” In RecSys 2017.

class recbole.model.sequential_recommender.transrec.TransRec(config, dataset)[source]

TransRec is translation-based model for sequential recommendation. It assumes that the prev. item + user = next item. We use the Euclidean Distance to calculate the similarity in this implementation.

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(user, 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

gather_last_items(item_seq, gather_index)[source]

Gathers the last_item at the specific positions over a minibatch

input_type = 2
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