Analysis of entry behavior of students on job boards in Japan based on factorization machine considering the interaction among features

Tomoya Sugisaki, Yuri Nishio, Kenta Mikawa*, Masayuki Goto, Takashi Sakurai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Job-hunting activities in Japan are different from those in other countries. The features of this are the simultaneous recruitment of new graduates, joining the company in April, and the use by most students of such resources as employment information websites. In recent years, website job boards for new graduates have provided Japanese students with assistance in finding companies for which they want to work. On these boards, students can bookmark companies that they are interested in before deciding to apply. After bookmarking, a company bookmarked by a user can examine the information again later. However, even if the students rate various companies, many of these bookmarks do not lead to job applications. In other words, this can be regarded as a lost opportunity for gaining job applications from the perspective of the company. It is important for companies to gain as many job applications as possible to be successful in their recruitment activities. Therefore, a method of analyzing the entry behavior of students on job boards using factorization machines is proposed. The model predicts whether a student will submit a job application to a company. The prediction is based on student attributes and activity information, as well as information about the companies that they are interested in, as input variables. The interactions between input variables are also considered in making the prediction. In addition, the method supports student job-hunting activities and company measures for targeting students. To clarify the proposed model, analytical experiments were conducted with actual data from a website job board for new graduates.

Original languageEnglish
Article number1988381
JournalCogent Engineering
Issue number1
Publication statusPublished - 2021


  • Big data
  • Machine Learning
  • Marketing
  • Statistics & Probability
  • Systems & Control Engineering
  • factorization machines
  • management information
  • prediction

ASJC Scopus subject areas

  • General Computer Science
  • General Chemical Engineering
  • General Engineering


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