Reinforcement Learning Based Multi-Stage Ad Sorting and Personalized Recommendation System Design

This paper constructs a multi-stage advertisement sorting and personalized recommendation system based on reinforcement learning. By introducing deep reinforcement learning to optimize the sorting strategy and integrating multi-dimensional user profiles to achieve recommendation refinement, the user conversion rate is improved while real-time performance is guaranteed. The system architecture covers data processing, model training and online deployment. Experimental results show that the method is better than the traditional model in terms of click rate, sorting quality and system stability, and has strong application value.