Developing a Modular AI Framework to Enhance Scalability and Personalization in Next-Generation Reward Platforms

Abstract

The paper presents the design of a scalable AI system that can enhance the platform of rewards and personalization. The research is based on a machine learning algorithm and aims at predicting user engagement with the use of several factors, including interaction time, reward points, and feedback score. A Random Forest Classifier model is fitted on a suitable data, with the results showing that moderate accuracy and performance are achieved. Model performance is interpreted using visualizations such as confusion matrices, feature importance plots, etc. The paper indicates the prospects of AI in expanding rewards platforms, even though other optimization and tests with the real world are still required.