  The LinkedIn Salary product was launched in late 2016 with the goal of
providing insights on compensation distribution to job seekers, so that they
can make more informed decisions when discovering and assessing career
opportunities. The compensation insights are provided based on data collected
from LinkedIn members and aggregated in a privacy-preserving manner. Given the
simultaneous desire for computing robust, reliable insights and for having
insights to satisfy as many job seekers as possible, a key challenge is to
reliably infer the insights at the company level when there is limited or no
data at all. We propose a two-step framework that utilizes a novel, semantic
representation of companies (Company2vec) and a Bayesian statistical model to
address this problem. Our approach makes use of the rich information present in
the LinkedIn Economic Graph, and in particular, uses the intuition that two
companies are likely to be similar if employees are very likely to transition
from one company to the other and vice versa. We compute embeddings for
companies by analyzing the LinkedIn members' company transition data using
machine learning algorithms, then compute pairwise similarities between
companies based on these embeddings, and finally incorporate company
similarities in the form of peer company groups as part of the proposed
Bayesian statistical model to predict insights at the company level. We perform
extensive validation using several different evaluation techniques, and show
that we can significantly increase the coverage of insights while, in fact,
even improving the quality of the obtained insights. For example, we were able
to compute salary insights for 35 times as many title-region-company
combinations in the U.S. as compared to previous work, corresponding to 4.9
times as many monthly active users. Finally, we highlight the lessons learned
from deployment of our system.
