Bayesian multi-view models for member-job matching and personalized skill recommendations

Inefficiencies in the labor market such as friction in matching members to jobs and the existence of skill gaps in various sectors of the economy are considered to be major problems facing economies today. The central premise of our work is that increasing the productivity of a member of the workforce (and thereby of the economy as a whole) crucially depends on identifying and recommending skills whose acquisition will yield the highest utility gains for that member. To this end, we develop novel unsupervised Bayesian multi-view models BayesMatch and NpBayesMatch which can match members to other similar members as well as relevant jobs using shared features. The matching step is followed by a skill recommendation step SkillR which makes demand-based skill recommendations to members. Our extensive quantitative evaluation using a rich dataset comprised of member profiles and job postings from LinkedIn suggests that skill recommendations made by our algorithm are highly correlated with skills demanded in heldout future jobs compared to those made by traditional collaborative filtering algorithms that do not utilize information about skill demand. This indicates that either members of the workforce do not have skills demanded by jobs or do not have enough information about which are the best skills to signal for competing in the labor market.