计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211100266-7.doi: 10.11896/jsjkx.211100266
王泽卿1, 季圣鹏1, 李鑫2, 赵子轩1, 王鹏旭1, 韩霄松1,3
WANG Ze-qing1, JI Sheng-peng1, LI Xin2, ZHAO Zi-xuan1, WANG Peng-xu1, HAN Xiao-song1,3
摘要： 近年来,随着我国高考人数逐年增多,考生对高考志愿填报服务的需求日益增加。面对海量的院校填报信息,考生往往很难在短期内做出比较符合自身意愿的合理选择,进而导致报考事故的发生。因此,针对高考志愿报名问题,在爬取历年高考录取数据的基础上,提出一种基于分数线预测的多特征融合推荐算法(Reco-PMF)。该算法首先利用历年高校最低投档位次,通过BP神经网络预测报考年份各高校最低投档位次以及最低投档分数线,然后根据考生分数进行院校初筛,进而构建3种与录取分数相关的特征,结合院校软科排名,通过遗传算法进行权值寻优,得到不同院校的录取概率,并在此基础上定义推荐度实现为考生进行不同录取风险层次的高校推荐,形成完整的推荐结果。实验结果表明,基于BP神经网络的高校录取分数预测算法在不同误差限下的表现均优于其他算法;相比百度和夸克的已有服务,所提算法在多层次测试分数下,平均录取率分别提升14.8%和24.1%,同时成功录取院校的平均位次分别提升了99名和87名。
中图分类号:
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