Research on Algorithm Recommended by Online Education for Big Data

Abstract

“Big data” is becoming a hot topic in the Internet. The long tail problem of the massive online courses also becomes the biggest headache for operation team of online education. The manner in which the reader wants most courses show to be presented before the user is the key to improve the quality of online edu-cation. Personalized recommendation system is to discover the readers interests tendency based on the existing user data, project data, and interactive data, thus to provide personalized product recommendation for readers. This article is based on the two kinds of algorithms, namely the content and the collaborative filtering recommendation to propose an improved integration scheme, which can make good use of existing data to discover the useful knowledge for readers’ recommendation. The method firstly solves the sparsity problem in traditional collaborative filtering, and meanwhile we start from the global structure relation of course, to analyze the relationship between the reader and the course more comprehensively. The algorithm to improve the accuracy of recommendation from multiple angles, and provides a feasible method for precise recommendation of online educational video.