AI-Powered Content Recommendation System Based on User Reading Speed for Digital Magazine

Abstract - Content recommendation systems have greatly enhanced user interaction in digital media, but they usually do not take into account real-time user behaviour, especially reading speed, resulting in less-than-optimal content recommendations. Current solutions emphasize static user preferences but fail to dynamically update recommendations in response to real reading behaviour. This paper introduces an AI-driven content recommendation system designed for digital magazines, combining real-time reading speed analysis and machine learning-based content personalization. The system utilizes adaptive learning algorithms to track user engagement, analyse reading time, and dynamically update content recommendations for a smooth reading experience. A tailored fine-tuned model is built specifically for forecasting user content preferences grounded in different reading speeds so that recommendations are personalized to complement unique reading behaviours. The whole pipeline is implemented as a cloud-based API that supports scalability, accessibility, and easy incorporation into digital publishing platforms. With optimized delivery of content via real-time behavioural insights, the system maximizes user engagement, retention, and overall satisfaction of reading. Benchmark tests show dramatic gains in recommendation accuracy and user engagement, especially for readers of digital magazines who want a personalized content experience.