Research On The Self-learning System For The Secondary Vocational Schools Based On Collaborative Filtering Technology

Self access learning is an important way for people to acquire knowledge.Recently,many universities have set up autonomous learning system,and with the application of increasing depth,number of learning resources in the system so that students may have to grow with each passing day,spend a lot of time to search to learn in the process of autonomous learning resources.Through the research of autonomous learning system to build more schools can be found,the resource push method mainly includes Top-N recommendation method,query keyword method and recommend the latest resources,these methods can satisfy the needs of learners in a certain extent,but insufficient in terms of personalized recommendation.In this paper,through the introduction of collaborative filtering technology to enhance the autonomous learning ability and recommendation system,through the project score prediction and content filtering recommendation method optimization recommendation algorithm,to reduce the impact of collaborative filtering technology in the application of sparse data must be faced with the problem of the cold start.At the same time,creates a hidden user score model based on learning behavior,including their recommendation and collection reflects the learner preference quantifiable resources score,using these scores to modify resources score,so as to enhance the system of personalized recommendation effect.According to the proposed optimization recommendation algorithm,combined with the vocational middle school students autonomous learning system subsystem is implemented,the results show that the method proposed in this paper can greatly alleviate the influence on the recommendation effect brought by the data sparse and cold start problems,and improve the efficiency and accuracy of personalized recommendation.