Big data-oriented film and television recommendation system and algorithm design

Abstract

In today’s Internet age, the explosive growth of video resources makes users face a large number of video resources every day. However simple data search can no longer accurately obtain the data users really want. In some respects, the large amount of video data reduces the effective use of video resources by people. It is an urgent problem to provide users with better video recommendation services through data mining. This paper studies the film and television recommendation system and algorithm design oriented to big data, consults related literatures to have a simple understanding of the structure of the film and television recommendation system, then optimizes the recommendation algorithm, and designs the film and television recommendation system according to the optimized algorithm. Then the designed system is tested. The test results show the accuracy of the recommended system is increased by 2% after the improved algorithm. The stability of the recommended algorithm is not very high from the fluctuation degree of the error bar.