Film and television art innovation in network environment by using collaborative filtering recommendation algorithm

Abstract

Abstract With the continuous development of network information technology, people's dependence on network information is becoming stronger and stronger. The information on the Internet shows a trend of explosion, and information overload has also become a research hotspot. Due to the defects of cold start and sparse data, the traditional personalized recommendation algorithm will show the problem of accuracy degradation in the face of excessive information. Therefore, the traditional methods have been unable to adapt to the current needs of literature and art analysis. The goal of speech enhancement is to remove noise interference from noisy sounds and extract pure sounds as much as possible. Speech enhancement can reduce sound distortion, improve sound quality, and reduce hearing fatigue. At present, voice enhancement technology is widely used in products and fields such as mobile communications, computers, smart phone devices, and smart homes. First, this article will briefly introduce the artistic analysis of film and television works. Starting from the main characteristics of film and television works, according to the characteristics of various data lists based on visualization and visual data mining. Through visual data mining, the experimental data set used in this article is constructed based on various data types such as the main narrative element data set and the character action data set.