Abstract
In this paper, a personalized online education platform based on a collaborative filtering algorithm is designed by applying the recommendation algorithm in the recommendation system to the online education platform using a cross-platform compatible HTML5 and high-performance framework hybrid programming approach. The server-side development adopts a mature B/S architecture and the popular development model, while the mobile terminal uses HTML5 and framework to implement the function of recommending personalized courses for users using collaborative filtering and recommendation algorithms. By improving the traditional recommendation algorithm based on collaborative filtering, the course recommendation results are more in line with users' interests, which greatly improves the accuracy and efficiency of the recommendation. On this basis, online teaching on this platform is divided into two modes: one mode is the original teacher uploads recorded teaching videos and students can learn by purchasing online or offline download; the other mode is interactive online live teaching. Each course is a separate online classroom; the teacher will publish online class information in advance, and students can purchase to get classroom number and password information online.
Highlights
With the exponential growth of online educational resources, information is changing from text form to multimedia forms such as pictures, audio, video, and online live broadcast; the quality of information and how to screen information have become an urgent problem to be solved [1]
Under the double pressure of work and study, the realization of the personalized recommendation of educational resources has become the primary problem that needs to be solved in intelligent education. e application of a personalized recommendation system can effectively solve the problem of cognitive overload or vagueness when users are learning online, which can greatly improve resource utilization and user learning efficiency
As a key technology to solve the problem of cognitive overload or disorientation during online learning, the personalized recommendation system has become the focus of current research on how to combine with online teaching
Summary
With the exponential growth of online educational resources, information is changing from text form to multimedia forms such as pictures, audio, video, and online live broadcast; the quality of information and how to screen information have become an urgent problem to be solved [1]. This paper investigates how to introduce the personalized recommendation technology widely used in the commercial field in online education and designs and implements a personalized education platform based on a collaborative filtering algorithm On this basis, online teaching on this platform is divided into two modes: one is the original teacher uploads recorded teaching videos; students can learn by purchasing online or offline download; the other is interactive online live teaching; each course is a separate online classroom; the teacher will publish online class information in advance; students can purchase the classroom number and password information, online learning. It becomes difficult to search for the resources you need in the huge dataset Given this situation and the growing needs of users, recommendation functions are gradually being integrated into the development of online education platforms. Engine Library (i) Correlated alarms and events (ii) Integrate with subsystems (iii) Root cause analysis (iv) Best practices
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