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The LinkedIn Salary product was launched in late 2016 with the goal ofproviding insights on compensation distribution to job seekers, so that theycan make more informed decisions when discovering and assessing careeropportunities. The compensation insights are provided based on data collectedfrom LinkedIn members and aggregated in a privacy-preserving manner. Given thesimultaneous desire for computing robust, reliable insights and for havinginsights to satisfy as many job seekers as possible, a key challenge is toreliably infer the insights at the company level when there is limited or nodata at all. We propose a two-step framework that utilizes a novel, semanticrepresentation of companies (Company2vec) and a Bayesian statistical model toaddress this problem. Our approach makes use of the rich information present inthe LinkedIn Economic Graph, and in particular, uses the intuition that twocompanies are likely to be similar if employees are very likely to transitionfrom one company to the other and vice versa. We compute embeddings forcompanies by analyzing the LinkedIn members' company transition data usingmachine learning algorithms, then compute pairwise similarities betweencompanies based on these embeddings, and finally incorporate companysimilarities in the form of peer company groups as part of the proposedBayesian statistical model to predict insights at the company level. We performextensive validation using several different evaluation techniques, and showthat we can significantly increase the coverage of insights while, in fact,even improving the quality of the obtained insights. For example, we were ableto compute salary insights for 35 times as many title-region-companycombinations in the U.S. as compared to previous work, corresponding to 4.9times as many monthly active users. Finally, we highlight the lessons learnedfrom deployment of our system.

How LinkedIn Economic Graph Bonds Information and Product: Applications in LinkedIn Salary