Personalized Recommendation and Interaction of Digital Media Based on Collaborative Filtering Algorithm

This article aims to explore the personalized recommendation and interaction methods of digital media based on CAD (Computer Aided Design) and RL (Reinforcement Learning). In this article, the related theories of CAD and RL are first deeply analyzed, and their potential application in personalized recommendation and interaction with digital media is discussed. Then, build a personalized recommendation model of digital media based on CAD and RL and realize accurate and personalized recommendation service by extracting the characteristics of digital media content, analyzing user behaviour data, and designing a reasonable RL algorithm. At the same time, this article also explores the optimization strategy of digital media interaction technology to improve the interactive experience between users and recommendation systems. The results show that the digital media recommendation and interaction method based on CAD and RL is significantly superior to the traditional methods in recommendation accuracy and user satisfaction. This method can capture users' interest preferences more accurately, provide personalized recommendations that are more in line with users' needs, and enhance users' experience through real-time interaction. © 2024, CAD Solutions, LLC. All rights reserved.