Digital Media Information Recommendation Model based on Artificial Intelligence Technology

As the amount of information in digital media continues to increase, users are facing an increasingly serious problem of information overload. How to accurately find the content that users are interested in from the vast amount of information is becoming increasingly important. Traditional information recommendation methods often fail to fully capture the similarities between users and the correlation between items, while the application of artificial intelligence technology, especially collaborative filtering algorithms, can help content providers better understand user needs and optimize content production and distribution strategies, thereby achieving a win-win situation for users, content providers and platforms. This article focuses on the application of coordinated filtering algorithms in digital media recommendation, and explores in detail user behavior analysis and data mining. Finally, through two sets of simulation experiments, the following conclusions are drawn: The overall average improvement in the accuracy index of this algorithm is 9.77%, while the overall average improvement in the recall index of the recommended model is 7.85%. This research not only helps improve the user experience and operational efficiency of digital media platforms, but is also of great significance in promoting the development of the digital economy.