Personalized recommendation of film and television culture is an important content to meet people’s daily cultural needs and social information. Promoting the personalized recommendation of film and television culture is conducive to promoting the more efficient use of network resources. However, in recent years, the film and television culture industry has developed rapidly, and the production of film and television culture has also increased year by year. How to quickly and accurately find the user’s favorite film and television culture in the massive film and television cultural data has become an urgent problem to be solved. Aiming at the shortcomings of the film and television culture recommendation system, this paper proposes a new personalized recommendation algorithm for film and television culture based on an intelligent classification algorithm. Based on the preliminary screening results of the traditional collaborative filtering recommendation algorithm, the user data and video data are used as input and the video score as output, which is further filtered by a convolutional neural network. Finally, selecting the film and television culture recommendation set that is most suitable for the current user can also make up for the cold start problem of collaborative filtering at the beginning of the system operation. The simulation experiment is carried out. The experimental results show that the personalized recommendation algorithm based on an intelligent classification algorithm improves the scoring accuracy by 0.15, which indicates that the designed film and television culture recommendation system has a good application effect.

中文翻译：

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基于智能分类算法的影视文化个性化推荐

影视文化的个性化推荐是满足人们日常文化需求和社会信息的重要内容。促进影视文化的个性化推荐，有利于促进网络资源的更有效利用。但是，近年来，影视文化产业发展迅速，影视文化的生产也逐年增加。如何在海量影视文化数据中快速准确地找到用户最喜欢的影视文化已经成为亟待解决的问题。针对影视文化推荐系统的缺陷，提出了一种基于智能分类算法的影视文化个性化推荐算法。根据传统协同过滤推荐算法的初步筛选结果，将用户数据和视频数据用作输入，将视频得分作为输出，然后通过卷积神经网络对其进行过滤。最后，选择最适合当前用户的影视文化推荐集还可以弥补系统运行开始时协同过滤的冷启动问题。进行了仿真实验。实验结果表明，基于智能分类算法的个性化推荐算法将评分精度提高了0.15，