Numerous job openings have been posted online, indicating the increasing importance of job recommendations. Recently, job seekers 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 job seeker desires. Although limited research has employed both (1) and (2) simultaneously, no sophisticated job recommendation method leverages multiple types of interactions and explicit preferences to achieve high accuracy. 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. Furthermore, the integration method of the latent representations obtained from each of the two modules addresses the consistency of user preferences and the similarity with job postings, enabling a more accurate estimation of user preferences. Experimental results on our dataset constructed from an actual job search website show that our proposed model outperforms the best baseline by 31.4% and 32.8% in terms of Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain@5 (nDCG@5), respectively. We have released our source codes.11https://github.com/saitoxu/JME-ESWA.