摘要
近年来,我国高考人数逐年增加,竞争也越来越激烈,高考志愿填报的重要性不言而喻。针对当前高考志愿填报存在的问题,在对高考志愿数据进行调研的基础上,提出了一种HHRA混合推荐算法。该算法首先构建用户特征矩阵,并采用min-max法做标准化处理;其次采用改进的皮尔逊相关系数进行相似度计算,并生成推荐集;然后对不同志愿的录取概率进行计算,并按录取概率进行院校志愿层次划分;最后从原始志愿表中提取考生偏好特征,计算偏好相似度,得到最终的志愿推荐结果。研究表明,采用HHRA算法能够更好地利用分数,满足用户个性化的志愿需求。
In recent years,the number of people taking the college entrance examination in China has been increasing year by year,and competition has become increasingly fierce.The importance of filling out college entrance examination application is self-evident.A HHRA hybrid recommendation algorithm is proposed based on a survey of college entrance examination volunteer data to address the current problems in filling out college entrance examination applications.The algorithm first constructs a user feature matrix and uses the min max method for standardization;Secondly,an improved Pearson correlation coefficient is used for similarity calculation and a recommendation set is generated.Then calculate the admission probabilities of different volunteers,and divide the levels of college volunteers according to the admission probabilities.Finally,extract candidate preference features from the original volunteer table,calculate preference similarity,and obtain the final volunteer recommendation result.Research has shown that using the HHRA algorithm can better utilize scores and meet users'personalized volunteer needs.
作者
温创新
黄桂萍
胡舟
Wen Chuangxin;Huang Guiping;Hu Zhou(School of Digital Technology,Xiangtan Institute of Technology,Xiangtan 410219,China)
出处
《现代计算机》
2024年第5期50-55,共6页
Modern Computer
基金
湖南省教育科学“十四五”规划2021年度国家教育考试研究专项立项课题(XJK21BKS015)。
关键词
高考志愿
HHRA算法
推荐算法
Jaccard系数
college entrance examination volunteer
HHRA algorithm
recommendation algorithm
Jaccard coefficient
