Exploiting job transition patterns for effective job recommendation

E-recruitment sites such as LinkedIn, Reed, and Indeed have a huge number of professional resumes from job seekers and job openings posted by recruiters. In this situation, it is a very time-consuming task for job seekers to find job openings that are well matched to their careers and desired conditions. Accordingly, active studies on job recommendation (JR) have been conducted recently. In this paper, we address the important property of transition patterns in JR that previous studies have overlooked. To incorporate the property into JR, we first propose two data modeling methods of adjacent pairing and all paring that represent a career path of a job seeker as a set of job pairs. Then, we propose frequency-based and graph-based methods of preference inference based on the data modeling methods. Finally, we develop four recommendation approaches, AdjacentFreq, AllFreq, AdjacentGraph, and AllGraph, each of which is a combination of two data modeling methods and two preference inference methods. Through extensive experiments using a real-life dataset, we show that our proposed approaches effectively address the unique property of JR. Also, we show that JR utilizing the transition information provides accuracy higher than JR not using the information.