Currently, most university students in Japan use Internet portal sites for job-hunting activities. However, job-hunting activities are sometimes prolonged owing to a mismatch between a student and the company requirements. To solve this problem, it is important to find the students who may not be able to finish job-hunting early; this goal can be achieved by utilizing user behavior log data stored on an Internet portal site. This study proposes appropriate statistical model based on a latent class model. Specifically, we also apply clustering approach and takes account of time-series variation. The proposed model enables us to analyze entry patterns from the viewpoint of time-series variation of job-hunting activities and to predict the finish date of job-hunting for each cluster. Through the simulation experiments, the effectiveness of the proposed method was clarified. We used actual data of students' activities from an Internet portal site to demonstrate the effectiveness of the proposed method that considers the time series of the entry tendency of student users. By considering the time shift of students' preferences, it became possible to extract students who tend to struggle in job-hunting activities. It is possible to specify students who should be supported by using the proposed model.
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