Research And Implementation Of Online Learning System Based On Personalized Recommendation Of Hybrid Algorithm

With the rapid development of the Internet today,we are increasingly dependent on the use of mobile devices.People use mobile devices to serve every aspect of our lives.Among them,online learning is currently the most popular field.We integrate learning resources and provide students and teachers with online learning.Teachers and students can access education services through this platform.Because the convenient use of mobile devices is not tied to the learning location,it is convenient to carry,so that learners can use fragmented time to learn.In this informal learning,students can reduce Cognitive load on learning.However,the increase in the type and number of learning resources has caused the problem of information overload.Therefore,we must make personalized recommendations for students' learning resources.At present,the applications of personalized recommendations are in e-commerce and online education.Few,and not perfect,for example,collaborative filtering algorithms have problems with sparse data and cold starts.Firstly,this paper analyzes the requirement of the online learning recommendation system based on hybrid algorithm.Before the requirement analysis,we need to understand the user's requirement of the online learning system through the market survey APP,then the overall design of the system,the system is divided into application layer and service layer.The application layer is corresponding to students,teachers and other mobile clients.MVC is divided into structure design,which provides data from service layer to application layer.Finally,the functions of User Login,note interactive discussion and self-learning are realized through requirement analysis.For the background,the recommended algorithms on mobile devices use a combination of compatible HTML5 and high performance MUI programming to design a learning platform for mobile devices,while the server uses a b / s architecture,make it easier to develop.The server side also provides the service interface for the iOS client and the web side,and realizes the interaction with the web client and the iOS client,and then carries on the test to each function in the system,the test results meet the design requirements of mobile online learning.In the process of realizing the whole system,the most important thing is to add the hybrid algorithm into the system,and realize the personalized recommendation to users.Before choosing the Hybrid Algorithm,we should study and analyze a large number of recommendation Algorithms,deeply study the advantages of the hybrid algorithm,find out the problems that the users may encounter in the process of using it,and design and implement it in detail,it includes the selection of the recommendation algorithm and the extraction of the user's data in the process of use.The selection method should start from the similarity of the user and the learning resources,and select multiple algorithms according to different users to match the user's needs,then according to the accuracy of the data and the coverage of the indicators to the hybrid algorithm fusion evaluation.Finally,according to the end-user requirements,the system is developed and the recommendation Algorithm is implemented.To solve the current personalized recommendation of the existence of data sparse and cold start.The user can quickly and accurately find the learning resources when looking up.