Author(s)

Shang Ma

Corresponding Author

Shang Ma

Abstract

This study investigates the application pathways and algorithmic mechanisms of deep reinforcement learning (DRL) in intelligent recommendation systems. By introducing a state–action–reward framework, user interactions are modeled as dynamic sequential processes to construct a policy network oriented toward long-term user value optimization. Models such as Deep Q-Network (DQN) and Actor-Critic are employed to iteratively learn from user feedback, enhancing the stability and generalization ability of recommendation systems. Experimental validations across typical application domains—e-commerce, media content delivery, and educational platforms—demonstrate the method's integrated advantages in addressing cold-start scenarios, adapting to dynamic user interests, and optimizing for multi-objective targets. The results indicate that DRL significantly improves the flexibility and sustainability of recommendation strategies, showing strong potential for cross-domain deployment.

Keywords

Deep Reinforcement Learning; Intelligent Recommendation System; Policy Optimization; Behavior Modeling; Multi-Scenario Application