Abstract: Methods and apparatuses are described for determining future user actions using time-based featurization of clickstream data. A server captures clickstream data corresponding to web browsing sessions and converts the clickstream data into tokens by identifying each unique URL and parsing each unique URL into tokens. The server generates a frequency matrix based upon the tokens, and generates a latent feature vector for each URL in the session based upon the frequency matrix. The server merges the latent feature vectors and the clickstream data into an aggregate clickstream vector set for a user. The server assigns time-decayed weight values to each latent feature vector in the aggregate clickstream vector set. The server combines the time-decayed latent feature vectors to generate a clickstream embedding for the user, and executes a machine learning model using the clickstream embedding to generate one or more predicted actions of the user.

Abstract: A computer-implemented method is provided for determining surrogate ground truth to enable fairness evaluation after completion of a campaign of interest. The surrogate ground truth indicates individuals who should have been contacted by the campaign of interest. The method includes receiving data for the campaign of interest and data for a previous campaign in relation to a population group selected for the previous campaign. The method also includes generating, before commencement of the campaign of interest, control and treatment models trained based on data collected from the previous campaign. The method further includes calculating, after completion of the campaign of interest, the surrogate ground truth using the trained control and treatment models and data collected from the campaign of interest.