NAIS¶
- Reference:
Xiangnan He et al. “NAIS: Neural Attentive Item Similarity Model for Recommendation.” in TKDE 2018.
- Reference code:
https://github.com/AaronHeee/Neural-Attentive-Item-Similarity-Model
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class
recbole.model.general_recommender.nais.NAIS(config, dataset)[source]¶ Bases:
recbole.model.abstract_recommender.GeneralRecommenderNAIS is an attention network, which is capable of distinguishing which historical items in a user profile are more important for a prediction. We just implement the model following the original author with a pointwise training mode.
Note
instead of forming a minibatch as all training instances of a randomly sampled user which is mentioned in the original paper, we still train the model by a randomly sampled interactions.
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attention_mlp(inter, target)[source]¶ layers of attention which support prod and concat
- Parameters
inter (torch.Tensor) – the embedding of history items
target (torch.Tensor) – the embedding of target items
- Returns
the result of attention
- Return type
torch.Tensor
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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
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forward(user, item)[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
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
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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
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get_history_info(dataset)[source]¶ get the user history interaction information
- Parameters
dataset (DataSet) – train dataset
- Returns
(history_item_matrix, history_lens, mask_mat)
- Return type
tuple
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input_type= 1¶
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mask_softmax(similarity, logits, bias, item_num, batch_mask_mat)[source]¶ softmax the unmasked user history items and get the final output
- Parameters
similarity (torch.Tensor) – the similarity between the histoy items and target items
logits (torch.Tensor) – the initial weights of the history items
item_num (torch.Tensor) – user hitory interaction lengths
bias (torch.Tensor) – bias
batch_mask_mat (torch.Tensor) – the mask of user history interactions
- Returns
final output
- Return type
torch.Tensor
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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
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reg_loss()[source]¶ calculate the reg loss for embedding layers and mlp layers
- Returns
reg loss
- Return type
torch.Tensor
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softmax(similarity, logits, item_num, bias)[source]¶ softmax the user history features and get the final output
- Parameters
similarity (torch.Tensor) – the similarity between the histoy items and target items
logits (torch.Tensor) – the initial weights of the history items
item_num (torch.Tensor) – user hitory interaction lengths
bias (torch.Tensor) – bias
- Returns
final output
- Return type
torch.Tensor
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user_forward(user_input, item_num, repeats=None, pred_slc=None)[source]¶ forward the model by user
- Parameters
user_input (torch.Tensor) – user input tensor
item_num (torch.Tensor) – user hitory interaction lens
repeats (int, optional) – the number of items to be evaluated
pred_slc (torch.Tensor, optional) – continuous index which controls the current evaluation items, if pred_slc is None, it will evaluate all items
- Returns
result
- Return type
torch.Tensor
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