Abstract: Computerized systems and methods are provided for determining cumulative success-based recommendations for repeat users. One such method includes determining user and item latent-features based on matrix factorization applied to matrices that include recommendation and feedback events. The feedback events indicate previously provided user preferences for at least a portion of the items. An item-recommendation policy is determined based on a cumulative metric that includes an expected value for the accumulation of stochastic user-item rewards associated with future (or subsequent) recommendations. The accumulation of the rewards is based on the user latent-features, the item latent-features, and the previous rewards included in the feedback events. Machine learning, such as reinforcement learning (RL), is employed to determine the item-recommendation policy based on the feedback events.

Abstract: In various example embodiments, a system and method for testing marketing strategies and approximate simulators offline for lifetime value marketing. In example embodiments, real world data, simulated data, and one or more policies that resulted in the simulated data are obtained. Errors between the real world data and the simulated data are determined. Using the determined errors, bounds are determined. Simulators are ranked based on the determined bounds, whereby a lower bound indicates a first simulator providing simulated data closer to the real world data then a second simulator having a higher bound.