The Application Of Deep Learning In The Recommendation System Of Students Education

With the development of Internet,the communication between people becomes more convenient,the area becomes more extensive,and the information becomes more real-time.While people exchange and obtain information,they will also generate a large amount of information to be transmitted on the network.Over time,more and more people and organizations participate in internet communication,and the amount of information generated is increasing every day.When people browse information on the Internet,because of the huge information resources,it is difficult to find the information resources suitable for their own interests.Based on such problems,the recommendation system has developed rapidly under the Internet conditions,which not only greatly facilitates people's lives,also meet people's growing and changing needs.The development of the Internet has also accelerated the development of education.Nowadays,in order to be able to acquire more diverse knowledge,students are also not content to daily classroom learning,gradually began to online learning.The Internet has laid a good foundation for the development of online education.The number of student users is increasing,and the learning resources are also increasing,so the online education platform is also facing the problem of information overload.To solve this problem,the recommended technology is adopted.In this paper,studies the traditional recommendation algorithm and deep learning recommendation algorithm.With student question resources as the background,combined with cognitive diagnosis,a personalized test question recommendation algorithm based on cognitive diagnosis and attention mechanism is designed.According to the student the answer to the question of information,through cognitive diagnosis model,it is concluded that the students' knowledge point mastery level,and then on the basis of the level of knowledge point mastery,combining the resources of the test questions of knowledge point information,stem information and the difficulty of the experts marking information,through neural network,personalized evaluation of the difficulty of the test question resources,recommend the test question resources to students.The main contents of this paper are as follows:(1)Review related literature and related algorithms.In this paper,the relevant recommendation literature is introduced one by one.The recommendation algorithm is divided into traditional recommendation algorithm and recommendation algorithm based on deep learning,which are explained according to their fields.(2)Design of personalized recommendation algorithm for question resources.The traditional online education platform learning resource recommendation algorithm is a content-based recommendation technology and a collaborative filtering-based recommendation technology,and does not directly target the knowledge points of the two.Therefore,this paper proposes the above personalized test recommendation algorithm based on cognitive diagnosis and attention mechanism,makes a personalized evaluation of the resource difficulty of questions through neural network.(3)This paper designs and experiments a learning resource recommendation system,and introduces the design objectives,requirements analysis,overall framework and module design of the system.