Job Recommendation Based on Multiple Behaviors and Explicit Preferences

A lot of job openings have been released online, which makes job recommendation more and more important. Recently, users often enter their preferences into job search websites to receive some job recommendations that they hope to apply for. To achieve this goal, the following two types of data are available: (1) auxiliary behavior data such as viewing job postings, bookmarking them and (2) explicit preference data such as conditions for a job that each user desires. Some researchers propose job recommendation by addressing either of them. However, they have not focused on simultaneously addressing both (1) and (2) so far. Given this point, we propose a method for job recommendation that employs auxiliary behavior data and each user’s explicit preference data simultaneously. Additionally, our proposed method addresses multiple behavior overlaps and refines the latent representations. Experimental results on our dataset constructed from an actual job search website show that our proposed model outperforms several state-of-the-arts as measured by MRR and nDCG. Our source code has been released
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