Using AI Prediction Technology to Optimize the Intelligent Recommendation Algorithm of Film and Television

With the rapid development of AI technology, the creation of movies and TV dramas is constantly changing and gradually becoming a new industrial revolution. How to use AI (Artificial Intelligence) prediction technology to optimize film production and market placement has become a concern for people. The article provides the application of AI prediction technology in intelligent prediction of film and television production from two perspectives: market prediction based on machine intelligence and market prediction based on cognitive intelligence. For intelligent recommendation algorithms for film and television, in TF-IDF (Term Frequency Inverse Document Frequency) natural language processing, the weight of words or phrases in the text is calculated by the product of word frequency and overall frequency. When evaluating the accuracy of AI prediction technology in film and television intelligent recommendation algorithms, a personalized recommendation list can be generated based on the user's viewing behavior. Then it calculates the accuracy of the recommendation algorithm by comparing the difference between the recommended content and the user's true preferences. For user 1, the total number of recommended content in the list is 6, and the actual number of recommended content viewed by the user is 5, with an accuracy rate of 0.83. For user 2, the total number of recommended content in the list is 11, and the actual number of recommended content viewed by the user is 9, with an accuracy rate of${0. 8 2}$. This article is conducive to promoting the transformation and leap of the film industry towards wisdom.