Research And Application Of Mobile Learning Model Based On Personalized Recommendation

In recent years,large-scale data,the rapid development of mobile technology so that people's lives can not be separated from the smart phone,because of its convenience,mobility and other characteristics,has been widely used in various fields.In the field of education,there is a new learning model that uses intelligent mobile terminals-"mobile learning".This kind of learning mode can make learners 'users learn at any time and any place according to their own preferences,so as to stimulate the learners' interest in learning.However,with the explosive growth of teaching resources,the storage of massive data in mobile learning and the effective use of resources has become the urgent problem to be solved.Therefore,this study aims at the application of mobile learning,the study of massive data storage and resource utilization.In the large data storage and processing,the use of large data cloud storage platform Hadoop as a mobile learning system storage platform,Hadoop in the small file storage there is a big flaw,this paper presents a small file storage optimization strategy to solve the small File storage bottlenecks.In the aspect of system development,Android technology is used to develop the learning end,and the system needs analysis and system architecture design,and the mobile learning model based on large data support is constructed.Finally,it is developed and implemented according to the model.In the development process,the teaching resource recommendation sub-module is designed,and the recommendation accuracy of the sub-module has been improved by studying and optimizing the collaborative filtering recommendation algorithm.This study not only effectively solves the problem of massive data storage,but also satisfies the individual needs of the learners in the learning process,so that the utilization rate of teaching resources has been improved.So that students can be more convenient and more efficient mobile learning.