Design And Implementation Of Ios Online Education System Based On Personalized Recommendation

With the rapid development of Mobile Internet, people become increasingly inclined to use mobile terminal to get information and service. The fragment of time is used by people through the mobile applications, and on-line education is a hot area of mobile applications.Through mode of on-line education on the side of mobile terminals the information of different educational resources, individual teachers and education agencies can be integrated and put in the internet. Student can use their mobile devices to easily get various educational services.However, as more and more educational platforms, types of educational resources and the increasingly number of educational resources appear,the problem of resource overload of educational information rises.Meanwhile, customer's requirement on the personalized content is getting higher, so it is necessary to launch the personalized educational resource into the market which is based on the customer's own demand and in line with his or her interest. At present, personalized recommendation in internet E-commerce is becoming more and more mature, but it is rarely studied and used in the on-line education field.First, this article makes an analysis on the founctional requirement of ios on-line educational system, and divides the whole on-line education into two layers of service layer and application layer.The application layer means the ios customer terminals including the terminals on the students, teacher, and agency respectively. With the help of ios delegation mode, the customer terminal can realize a MVC-type design, which lowers the coupling of data and view on the customer terminal side. Service layer offer data support to the customer terminal in the application layer. The service has packaged all the business logic needed in the on-line educational system. Http is used to keep communication between service layer and application layer.Then, the article shows the investigation and deep study of present recommending algorithm, from which the on-line education system features are summarized. Detailed design about the recommending modules is manifested, including the design and extraction of user behavior data, algorithm of recommending module and collection of related data. The selection of algorithm is based on the user similarity and curriculum similarity. Collaborative filtering recommendations based on users and objects respectively are selected,compared and optimized according to forecast accuracy and fraction of coverage. The algorithm has been tested in the TED data for its effectiveness in the on-line educational environment.At last, based on the project requirements, system development and real jobs are completed, and the system functions were tested.