  To cope with the accelerating pace of technological changes, talents are
urged to add and refresh their skills for staying in active and gainful
employment. This raises a natural question: what are the right skills to learn?
Indeed, it is a nontrivial task to measure the popularity of job skills due to
the diversified criteria of jobs and the complicated connections within job
skills. To that end, in this paper, we propose a data driven approach for
modeling the popularity of job skills based on the analysis of large-scale
recruitment data. Specifically, we first build a job skill network by exploring
a large corpus of job postings. Then, we develop a novel Skill Popularity based
Topic Model (SPTM) for modeling the generation of the skill network. In
particular, SPTM can integrate different criteria of jobs (e.g., salary levels,
company size) as well as the latent connections within skills, thus we can
effectively rank the job skills based on their multi-faceted popularity.
Extensive experiments on real-world recruitment data validate the effectiveness
of SPTM for measuring the popularity of job skills, and also reveal some
interesting rules, such as the popular job skills which lead to high-paid
employment.
