This is a guest post co-authored by Taylor Names, Staff Machine Learning Engineer, Dev Gupta, Machine Learning Manager, and Argie Angeleas, Senior Product Manager at Ibotta. Ibotta is an American technology company that enables users with its desktop and mobile apps to earn cash back on in-store, mobile app, and online purchases with receipt submission, linked retailer loyalty accounts, payments, and purchase verification.
Ibotta strives to recommend personalized promotions to better retain and engage its users. However, promotions and user preferences are constantly evolving. This ever-changing environment with many new users and new promotions is a typical cold start problem—there is no sufficient historical user and promotion interactions to draw any inferences from. Reinforcement learning (RL) is an area of machine learning (ML) concerned with how intelligent agents should take action in an environment in order to maximize the notion of cumulative rewards. RL focuses on finding a balance between

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