Study on Cultural Change and Adaptation of Media Talents in Film, Television and Art Media Driven by Emerging Technologies

Emerging media technologies, epitomized by short video intelligent recommendation algorithms, are catalyzing substantial transformations within the realm of film and television culture. This study delves into the secondary creation of film and television works facilitated by algorithmic recommendation technologies. Specifically, it introduces a novel short video intelligent recommendation algorithm founded on deep learning principles. This algorithm harnesses joint features derived from the scene and behavioral attributes of target short videos, employing a graph convolutional neural network to model the long-term and short-term preferences, thereby enabling intelligent recommendations for the secondary creation of film and television works. Further, this research designs and implements questionnaires to formulate research hypotheses and conducts surveys targeting both general film and television audiences and industry professionals. The objective is to scrutinize the profound alterations instigated by emerging algorithmic recommendation technologies in film and television culture and to assess the adaptability and acceptance of these changes among media practitioners. The findings reveal that both explicit personalization (t=9.26, P