College English Precise Teaching Model Using Combination Optimization Collaborative Filtering Algorithm

Abstract

With the rapid development of the Internet and various learning platforms, the explosive growth of resources in the field of education has made the recommendation of learning resources increasingly important, and it has gradually become the research focus of academic circles. This article develops an English precision teaching platform based on the CF algorithm of combinatorial optimization to accurately recommend English learning materials that meet the learners’ personal preferences and achieve precision teaching. This optimization algorithm takes full advantage of the background information of users, calculates the similarity between users, and forms the best neighbor set, and then recommends the learning resources evaluated by neighbors with similar interests to the target users, addressing the problem of data sparseness and cold start, which leads to a decline in recommendation accuracy. On different data sets, experimental results show that this method’s recommendation accuracy is 94.6%, which is higher than the CF algorithm of multifeature fusion by 4.5% and the traditional CF algorithm by 9.7%. The findings show that when applied to English instructional resources, this method is accurate and practical and that it can effectively recommend resources that are appropriate for users to achieve accurate CET.