Beyond Matching: Modeling Two-Sided Multi-Behavioral Sequences for Dynamic Person-Job Fit

Online recruitment aims to match right talents with right jobs (Person-Job Fit, PJF) online by satisfying the preferences of both persons (job seekers) and jobs (recruiters). Recently, some research tried to solve this problem by deep semantic matching of curriculum vitaes and job postings. But those static profiles don’t (fully) reflect users’ personalized preferences. In addition, most existing preference learning methods are based on users’ matching behaviors. However, matching behaviors are sparse due to the nature of PJF and not fine-grained enough to reflect users’ dynamic preferences.