A College Entrance Examination Major Score Recommendation Strategy Based on Collaborative Filtering

Under the background of the new National College Entrance Examination (NCEE) reform in China, a major score-based college entrance examination voluntary recommendation strategy is proposed using collaborative filtering. This strategy utilizes historical major score cutoffs and employs random forest to predict the current year’s scores. It clusters similar major preference groups based on these scores. Taking the Zhejiang NCEE reform from 2018 to 2022 as an example, data on universities and majors are collected. Experimental data includes student scores and ranks. Different levels of major recommendation sets are provided based on the students' scores and ranks. The results generated by this strategy are compared with mainstream recommendations in terms of accuracy and precision, demonstrating its effectiveness.