基于协同过滤的高考志愿推荐系统

College Entrance Examination Voluntary Recommendation System Based on Collaborative Filtering

近年来信息过载问题的出现使得个性化推荐技术应运而生,其中协同过滤推荐技术通过在用户和信息之间建立联系,被广泛应用于电子商务各个领域.而在高考志愿填报领域考生也存在无法高效的从诸多高校中选取适合自己的高校这一"信息过载"问题.为此,可以将协同过滤思想应用到高考志愿填报这一新领域,将考生看作是推荐系统中的用户,高校看作是系统中的项目,通过分析历年的考生志愿填报相关数据,从构建用户属性矩阵,查找邻居用户和产生推荐三个过程进行详细描述,并对实验产生的推荐结果进行分析,说明了推荐系统的有效性,也为进一步的研究工作奠定基础.

Information overload problem in recent years makes the personalized recommendation technology arise, the collaborative filtering recommendation technology by establishing contacted between the user and the information has been widely used in every field of e-commerce. And in the field of the college entrance examination voluntary students also have the 'information overload' problem, which means they cannot choose the suitable college from many colleges efficiently. Therefore, the idea of collaborative filtering is applied to this new field, take the students as users and colleges as the items in the recommendation system. By analysing students' voluntary reporting relevant data from the previous year, three processes of building user attributes matrix, finding the neighbor users and generating recommendation are described in detail. The recommendations results of the experiment show the effectiveness of recommendations systems, and it lays the foundation for further research work.