Abstract: In some aspects, the techniques described herein relate to a method including: receiving a vector, the vector including a plurality of features related to a user; predicting a return probability for the user based on the vector using a first predictive model; adjusting the return probability using a fitted sigmoid function to generate an adjusted return probability; and predicting a lifetime value of the user using the adjusted return probability and at least one other prediction by combining the adjusted return probability and the at least one other prediction.

Abstract: The example embodiments are directed toward improvements in predicting an ideal audience size. In an embodiment, a method is disclosed comprising receiving a set of users associated with an object attribute; selecting samples from the set of users; computing hit rates for the samples, a respective hit rate in the hit rates computed by calculating a total number of users in a respective sample associated with an interaction associated with the object attribute; and selecting a recommended sample from the samples, the recommended sample comprising a sample having an associated hit rate that meets a preconfigured hit rate threshold.

Abstract: The example embodiments are directed toward improvements in generating affinity groups. In an embodiment, a method is disclosed comprising generating probabilities of object interactions for a plurality of users, a given object recommendation ranking for a respective user comprising a ranked list of object attributes; calculating interaction probabilities for each user over a forecasting window; calculating affinity group rankings based on the probabilities of object interactions and the interaction probabilities for each user; and grouping the plurality of users based on the affinity group rankings.

Abstract: The example embodiments are directed toward predicting the lifetime value of a user using an ensemble model. In an embodiment, a system is disclosed, including a generative model for generating a first prediction representing a first lifetime value of a user during a forecasting period and a discriminative model configured for generating a second prediction representing a second lifetime value of the user during the forecasting period. The system further includes a meta-model for receiving the first prediction and the second prediction and generating a third prediction based on the first prediction and the second prediction, the third prediction representing a third lifetime value of the user during the forecasting period.