  Recently, due to the ubiquity and supremacy of E-recruitment platforms, job
recommender systems have been largely studied. In this paper, we tackle the
next job application problem, which has many practical applications. In
particular, we propose to leverage next-item recommendation approaches to
consider better the job seeker's career preference to discover the next
relevant job postings (referred to jobs for short) they might apply for. Our
proposed model, named Personalized-Attention Next-Application Prediction
(PANAP), is composed of three modules. The first module learns job
representations from textual content and metadata attributes in an unsupervised
way. The second module learns job seeker representations. It includes a
personalized-attention mechanism that can adapt the importance of each job in
the learned career preference representation to the specific job seeker's
profile. The attention mechanism also brings some interpretability to learned
representations. Then, the third module models the Next-Application Prediction
task as a top-K search process based on the similarity of representations. In
addition, the geographic location is an essential factor that affects the
preferences of job seekers in the recruitment domain. Therefore, we explore the
influence of geographic location on the model performance from the perspective
of negative sampling strategies. Experiments on the public CareerBuilder12
dataset show the interest in our approach.
