Reinforcement-Learning-Based Personalization Engine for Adobe Experience Clouds

Abstract

This research presents a Reinforcement Learning-Based Personalization Engine designed to enhance the provision of tailored digital experiences within Adobe Experience Cloud during customer journeys. Traditional rule-based or static machine-learning personalization techniques often struggle with changing their user behavior, limited information as well as shifting business goals. This means that these systems need to always be learning & changing in actual time. The proposed engine employs reinforcement learning as its principal decision-making structure, enabling the system to observe user interactions, predict intent & improve content selection through continuous feedback loops. The architecture works well with Adobe Experience Platform, Adobe Target along with Adobe Analytics. It uses customer profiles, event streams & content metadata to express state, and it sets rewards based on their engagement, conversion, and long-term value assessments. A multi-agent reinforcement learning architecture is used to balance exploration as well as exploitation among many other different audience groups, making sure that their personalization is both scalable & consistent. The methodology includes ways to encode states, deep reinforcement learning algorithms to optimize policies as well as an orchestration layer that makes sure that actual time decisions are in line with marketing goals & compliance requirements. A case study demonstrates the engine's successful utilization for tailored online and app experiences, highlighting improvements in click-through rates, dwell time as well as conversion rates compared to baseline models. Experimental results demonstrate that the RL agent proficiently adapts to evolving user behaviors, alleviates overfitting to ephemeral trends & delivers experiences that are more contextually aware. This study introduces a scalable, intelligent, and extensible customization technology that transforms Adobe Experience Cloud into a continuously evolving ecosystem, thereby improving significant & quantifiable user engagement.