LR

Reference:

Matthew Richardson et al. “Predicting Clicks Estimating the Click-Through Rate for New Ads.” in WWW 2007.

class recbole.model.context_aware_recommender.lr.LR(config, dataset)

Bases: ContextRecommender

LR is a context-based recommendation model. It aims to predict the CTR given a set of features by using logistic regression, which is ideally suited for probabilities as it always predicts a value between 0 and 1:

\[ \begin{align}\begin{aligned}CTR = \frac{1}{1+e^{-Z}}\\Z = \sum_{i} {w_i}{x_i}\end{aligned}\end{align} \]

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

forward(interaction)

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.

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

training: bool