Research On Collaborative Filtering Method For Learning Resource Recommendation

With the development of information technology,online learning platform is welcomed by learners because it can provide a good learning environment.However,the vast majority of online learning platforms can not push the required learning resources for learners,and learners can only learn step by step through the learning resources released by teachers on the platform.Collaborative filtering,as a commonly used method of pushing learning resources,fails to start from the learner's cognitive structure,resulting in the inaccuracy of recommended learning resources.Therefore,it is particularly urgent to carry out research on Learning Resource Recommendation methods.Based on a large number of literatures,this study combs the research status of collaborative filtering and learning resource recommendation,optimizes the existing problems of collaborative filtering recommendation,and presents a collaborative filtering method for learning resource recommendation.Firstly,the elements of recommendation learning resources are determined by collaborative filtering: judging learners' cognitive structure by rule space model of cognitive diagnosis,collecting and analyzing learners' online learning behavior data by online learning system,obtaining students' participation analysis table and resource scoring table,and calculating target learners' learning preferences;secondly,calculating learners' learning preferences by cosine similarity algorithm Similarity,T learners with the highest similarity are regarded as the neighbor set of target learners.Finally,the target learners' scores are predicted according to the neighbor set's score of resources,so that the N learning resources with the highest score are recommended to target learners to achieve the goal of accurate recommendation of learning resources.To verify the feasibility and effectiveness of this method,the collaborative filtering method for Learning Resource Recommendation is applied to the teaching practice of Algorithms and Programming in senior high school.The application results show that thismethod can provide students with learning resources with higher accuracy and improve their academic performance.