Personalized Marketing Recommendation System of New Media Short Video Based on Deep Neural Network Data Fusion

With the rapid development of mobile Internet, short video has become another darling after traditional webcast in recent years. How to make full use of short video for effective marketing has become a hot issue that academia and industry are paying close attention to. This article is mainly aimed at exploring practical new media through in-depth research and exploration of the specific implementation methods and strategies of short video marketing in social media, based on the advantages and characteristic models of short video marketing in social media. The strategy of short video marketing in social media, and the use of highly in-depth neural network analysis technology for the personalized marketing recommendation system of new media short videos, so as to better promote the use of social media short videos by enterprises or individuals. We have to learn from marketing activities. The experimental results of this article show that when the data volume reaches 80%, the performance of the VRBCH algorithm steadily improves, so the performance of the main F of the VRBCH algorithm is still relatively ideal when the data volume changes. Due to the high dilution of the experimental data set, the amount of data in the VRBCH algorithm has increased sharply by 30% to 35%, but the purchase rate of the marketing recommendation system is as high as 98%. Therefore, the system has high feasibility.