hopwise.model.general_recommender.line¶

Reference:: Jian Tang et al. “LINE: Large-scale Information Network Embedding.” in WWW 2015.
Reference code:: https://github.com/shenweichen/GraphEmbedding

Classes¶

`NegSamplingLoss`
`LINE`	LINE is a graph embedding model.

Module Contents¶

class hopwise.model.general_recommender.line.NegSamplingLoss¶

Bases: torch.nn.Module

forward(sign, score)¶

class hopwise.model.general_recommender.line.LINE(config, dataset)¶

Bases: hopwise.model.abstract_recommender.GeneralRecommender

LINE is a graph embedding model.

We implement the model to train users and items embedding for recommendation.

input_type¶

embedding_size¶

order¶

second_order_loss_weight¶

interaction_feat¶

user_embedding¶

item_embedding¶

loss_fct¶

used_ids¶

random_list¶

random_pr = 0¶

random_list_length¶

sampler(key_ids)¶

random_num(num)¶

get_user_id_list()¶

forward(h, t)¶

context_forward(h, t, field)¶

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

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

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