Personalized Film and Television Recommendation System Based on Big Data Platform

The emergence of big data platforms has alleviated the restrictions of space and time on people's enjoyment of music information services. The need to improve user experience has become very urgent. One of the effective solutions to information overload is the recommendation system, which can help people discover interesting content from complex information. Therefore, the purpose of this article is to study a personalized film and television recommendation system based on a big data platform. This article first introduces the core technology of big data through an overview of the basic theory of big data. And combined with the traditional film and television recommendation method, analyze the existing problems and shortcomings, and analyze and discuss it with the core technology of big data. This article systematically expounds the overall process, system composition design and algorithm analysis of the personalized film and television recommendation system based on the big data platform. Experimental research shows that the algorithm proposed in this paper is better than the traditional User-basedCF strategy and Item-basedCF strategy under the same number of movies recommended by Hit-rate.