Design of Digital Media Recommendation System Based on User Behavior Analysis and Collaborative Filtering Algorithm

Using digital interactive platform to design digital media recommendation system to improve the service quality of digital media. The optimal design method of digital media remote sensing is studied. Intelligent recommendation of digital media by combining big data mining and resource scheduling. In this paper, the simulation results show that the precision of collaborative filtering algorithm is higher among the three algorithms, followed by GA (Genetic Algorithm) and finally AI (Artificial Intelligence). When the number of iteration steps reaches 30, the recommended accuracy of this algorithm is 93.3%, 90.7% for GA and 83.8% for AI. Then, the user behavior feature detection model of digital media recommendation is established, and digital media recommendation is carried out in collaborative filtering algorithm. Finally, the simulation experiment shows that this method has superior performance in improving the recommendation ability of digital media.