Research On Individualized Recommendation Model Of Learning Resources In Mobile Learning Environment

In recent years,with the vigorous development of mobile communication technology and the rapid popularization of terminal equipment,mobile learning has become an important way for people to obtain daily resources,and provides a convenient way for individual learning.However,there are too many resources for learning with complicated under the mobile learning environment,it is easy to comes out the issues for trekking information and overloaded.It makes that difficult for learners to locate the required resources in a short time,and the time and energy of the learners are more expensive.The personalized recommendation of learning resources can improve the efficiency of the retrieval resources,save the time and energy of the learners,and help learners to find the target learning resources quickly and accurately.In addition,in order to make the learning resources recommended by the system to meet every learner's needs,personalized recommendation has become the current resources in the development and design of the core elements of concern,how to reflect the personalized recommendation of resources is a hotspot of current research.In order to solve the problem of information overload and improve the recommendation of individuality,combined with the analysis of the environmental characteristics of mobile learning,The comparison of several kinds of personalized resources recommendation algorithms which are more common in the present time,and based on the actual needs of learners,A individualized recommendation model of learning resources is designed with five dimensions.There are five dimensions based on situational factors,learner characteristics,learners' interest factors,learners' target factors and feedback update factors.A hybridrecommendation model for personalized resource recommendation is proposed.This paper expounds the reasons for the design of 5 factors.And this paper also calculates the influence of the 5 factors on the recommendation effect of learning resources and the recommended weights in the model by using the method of questionnaire survey.In the end,I hoping to provide a reference to the model structure for development of learning resources in the future.