Abstract: Systems and methods are provided for determining or predicting user demographic information using user behaviors through a Bayesian inference. A computing system may determine demographic information (such as age or gender) of a user based on a Bayesian update and a purchase or other user action by the user. In some embodiments, the computing system may determine the household composition of a user account based on multiple purchases by the user account. The computing system may generate recommendations for the user or the user account based on the demographic information of the user or the household composition of the user account.

Abstract: Systems and methods are provided for optimizing campaigns (such as marketing campaigns) based on both short term and long term behaviors of users. A computing system learns an incremental outcome prediction model using training data comprising a marketing campaign log entry of a subject user and another marketing campaign log entry of a corresponding user, which represents a counterfactual outcome for the subject user. A marketing campaign can be selected for another user using the learned model.