What question did this study set out to answer?

The aim is to explore how reinforcement learning can enhance recommendation systems through better decision-making and user interaction.

April 1, 2026

Application and improvement of reinforcement learning in intelligent recommender systems

Key Points

The aim is to explore how reinforcement learning can enhance recommendation systems through better decision-making and user interaction.
Overview of reinforcement learning applications in recommendations
Analysis of key technologies including user behavior modeling and strategy optimization
Discussion of innovative methods like deep reinforcement learning and multimodal data fusion
Reinforcement learning improves accuracy in recommendations by adapting to user behavior changes
Identified key challenges including reward function design and state representation
Highlighted development trends for real-time personalized recommendations

Abstract

Reinforcement learning, as a machine learning method that can continuously optimize the decision-making strategy by interacting with the environment, has been widely noticed and applied in intelligent recommendation systems in recent years. Compared with traditional recommendation algorithms, reinforcement learning can dynamically capture changes in user behavior and adjust the recommendation strategy in real time, so as to improve the accuracy of personalized recommendation and user experience. In this paper, we first overview the basic application of reinforcement learning in recommender systems, and explain the mechanism of constructing recommendation strategies and improving recommendation effects; then we focus on the analysis of the three key technologies and challenges: user behavior modeling and state representation, reward function design and feedback mechanism, and strategy optimization and exploration strategy; finally, we discuss the development trends of innovative methods based on deep reinforcement learning, multimodal data fusion, and personalized real-time recommendation. Finally, we discuss the innovative methods based on deep reinforcement learning, multimodal data fusion, and personalized real-time recommendation. Through systematic sorting and summarization, this paper aims to provide theoretical guidance and technical reference for the application of reinforcement learning in intelligent recommender systems, and to promote the further development and improvement of this field.

Ask AI

Helpful

Bookmark