文献类型：期刊文献

英文题名：A Job Recommendation System Based on Student and Category Similarity Computation

作者：Tan, Yang[1]; Zhu, Jiapeng[1]; Leng, Chunxia[1]; Gaglio, Salvatore[2]

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China; [2] Free University of Berlin, Berlin, Germany

年份：2023

卷号：122

起止页码：20

外文期刊名：Lecture Notes on Data Engineering and Communications Technologies

收录：EI(收录号：20222812342027)

语种：英文

外文关键词：Collaborative filtering

摘要：The expansion of enrollment in Chinese universities has further aggravated the pressure of graduates’ employment competition. Students have to spend lots of time seeking satisfactory jobs. There is a growing uncertainty in finding a job because education is biased toward basic knowledge, students lack both work experience and social background, and job market changes rapidly. Students tend to look for jobs that match their interests and skills. It’s difficult to apply traditional collaborative filtering recommendation algorithm directly. In this case, we adopt the method of recommendation based on students’ career preferences. Data collected from IT industry on 51job website are divided into 15 first-level categories and 246 second-level categories through a semi-manual method. After student information and job information are cleaned and captured by keywords, the degree of matching between the student and the second-level category is calculated, and interpretable recommendations are realized. ? 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.