Research And Implementation Of Personalized Recommendation System For Learning Resources

With the rapid development of the Internet,the online learning has gradually become a way of learning,which is widely recognized and adopted by the public.With the rapid increase of learning resources in the Internet,it is an urgent problem to help the learners find the learning resources that they are interested in quickly.Personalized recommendation technology is a recommendation technology,which recommends appropriate learning resources for learners based on learners' learning behavior.It has been valued by researchers in solving the problem of overloading learning resources and meeting the needs of different learners.Collaborative filtering algorithm is based on learners' score data of learning resources,recommends similar learning resources to different learners,and divides them into user-based collaborative filtering algorithm and item-based collaborative filtering algorithm.Through experiments,it is found that item-based collaborative filtering algorithm is more accurate in the learning resource recommendation.At the same time,considering that learning resources involved in the learning process have certain timing characteristics,frequent pattern mining technology is applied to the learning resource recommendation.The GSP algorithm can quickly excavate the learners' maximum frequent learning resources sequence and recommend learning resources that are most likely to be needed for different learners.Single recommendation algorithm can not get the satisfactory recommendation results.In this paper,the Hybrid recommendation algorithm based on collaborative filtering algorithm and GSP algorithm recommendation results is adopted.Through data set verification,this hybrid recommendation algorithm has a F index of about 34%,which is significantly improved compared with a single recommendation algorithm.Finally,the personalized recommendation system of learning resources is implemented.The hybrid algorithm based on item-based collaborative filtering algorithm and GSP algorithm is applied to the system,which achieves accurate recommendation of learning resources and good results.