The Study And Actualization Of Internet Learning Platform Based On Personalized Recommendation Of Learning Resources

With the rapid development of Internet and information technology,knowledge data showing explosive growth,how to exploit fast the learners interest or learning resources which they need most from massive data,and according to the characteristics of learners will recommend these resources to them,there will be the important practical significance for improving the learning efficiency and learning effect.The essay is the combination of learning style,learning process and cognitive level,the personalized learning resource recommendation network learning platform for the researching,designing,development and validation experiments based on the real implementation of the effective combination of "Internet plus education".This paper analyses the various personalized recommendation algorithm,mainly used in electronic commerce is widely used in collaborative filtering algorithm based on user recommendation algorithm,which aims at the cold start problems,using Felder-Silwerman's learning style inventory perception dimension testing,user registration information of tag extraction,TOP N has recommended the recommended learning resources;according to the data sparsity problem,mainly from the aspects of adding optimization of collaborative filtering algorithm learning process design,test design implicit rating score,resource initial value,in order to improve the recommendation accuracy and efficiency of recommendation.In addition,based on the optimized algorithm,a personalized learning resource recommendation model is designed,and the system requirements analysis,database design,functional module design,implementation and testing are carried out.Finally,the MAE average absolute error method is chosen as the evaluation criterion,and the effectiveness of the optimized personalized recommendation algorithm in the network learning platform is verified by experiments.