College English Precise Teaching Model Using Combination Optimization Collaborative Filtering Algorithm

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