Real-Time Personalized Ad Recommendation Based on User Behavioral Analysis

Real-time personalized ad recommendation systems are crucial for enhancing user engagement and satisfaction. To address the challenge of delivering highly relevant ads in a dynamic, large-scale environment, this paper proposes a novel approach that integrates real-time user behavior analysis with advanced time series modeling and stream processing techniques. Specifically, the system leverages Long Short-Term Memory (LSTM) networks to capture both short-term and long-term user preferences, ensuring accurate and personalized ad recommendations. By utilizing stream processing frameworks like Apache Kafka and Apache Flink, the system supports high-throughput data ingestion and low-latency processing, even under high user concurrency. Experimental results demonstrate that the proposed system outperforms traditional methods and state-of-the-art models in terms of recommendation accuracy, response time, and user satisfaction. This approach offers significant advantages in real-time ad delivery and provides a scalable, efficient solution for personalized advertising in large-scale applications.