Separation of models based on presence or absence of a feature set and selection of model based on same

摘要

Separate models are built to predict the likelihood of conversion based on the presence or absence of one or more features. For example, a first model may be built to predict the likelihood of conversion of a non-converter who has never visited an advertiser's website before and a second model may be built to predict the likelihood of conversion of a non-converter who has visited an advertiser's website before. To determine which model to apply to an entity, the consumption history of the entity is searched for the presence or absence of the one or more features used to separate the models. The entity's consumption history is then scored based on the applicable model to determine the likelihood of conversion.