Multi-Behavior Job Recommendation with Dynamic Availability
Yosuke Saito, Kazunari Sugiyama · 2023
In recent years, we can see a lot of job postings on the Internet, providing us with more diverse job opportunities. As a result, it is getting more and more difficult for job seekers to find job postings relevant to their preferences. Consequently, job recommendations play an important role to reduce the burden of job searching. Generally, job postings have a publication period, for example, 30 days. Then they have been expired since the positions were occupied. As a result, job seekers may be frustrated when they experience such situations as they cannot apply for the positions. This indicates that job seekers may have strong preferences for job postings even if their application behaviors cannot be observed. This kind of gap has not been investigated in the line of Multi-Behavior Recommendation. Therefore, in this work, we propose a new job recommendation model, called Multi-Behavior Job Recommendation with Dynamic Availability (MBJ-DA), which takes into account: (1) auxiliary behaviors other than an application behavior and (2) the influence of dynamic availability of job postings. MBJ-DA enables a more accurate estimation of each user’s actual preferences by explicitly distinguishing the noise potentially inherent in auxiliary behaviors. Furthermore, by explicitly considering the influence of the dynamic availability of job postings, MBJ-DA can mitigate biases resulting from the influence and estimate each user’s actual preferences more accurately. Experimental results on our dataset constructed from an actual job search website show that MBJ-DA outperforms several state-of-the-arts in terms of MRR and nDCG.