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Promotions and discounts have become key components of modern e-commerce platforms. For online travel platforms (OTPs), popular promotions include room upgrades, free meals and transportation services. By offering these promotions, customers can get more value for their money, while both the OTP and its travel partners may grow their loyal customer base. However, the promotions usually incur a cost that, if uncontrolled, can become unsustainable. Consequently, for a promotion to be viable, its associated costs must be balanced by incremental revenue within set financial constraints. Personalized treatment assignment can be used to satisfy such constraints. This paper introduces a novel uplift modeling technique, relying on the Knapsack Problem formulation, that dynamically optimizes the incremental treatment outcome subject to the required Return on Investment (ROI) constraints. The technique leverages Retrospective Estimation, a modeling approach that relies solely on data from positive outcome examples. The method also addresses training data bias, long term effects, and seasonality challenges via online-dynamic calibration. This approach was tested via offline experiments and online randomized controlled trials at Booking.coma leading OTP with millions of customers worldwide, resulting in a significant increase in the target outcome while staying within the required financial constraints and outperforming other approaches. CCS CONCEPTS • Computing methodologies → Learning from implicit feedback; • Information systems → Personalization.

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Many e-commerce websites use recommender systems to recommend items to users. When a user or item is new, the system may fail because not enough information is available on this user or item. Various solutions to this 'cold-start problem' have been proposed in the literature. However, many real-life e-commerce applications suffer from an aggravated, recurring version of cold-start even for known users or items, since many users visit the website rarely, change their interests over time, or exhibit different personas. This paper exposes the Continuous Cold Start (CoCoS) problem and its consequences for content-and context-based recommendation from the viewpoint of typical e-commerce applications, illustrated with examples from a major travel recommendation website, Booking.com.

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