Accepted Papers

Social Skill Validation at LinkedIn

Xiao Yan, Jaewon Yang, Mikhail Obukhov, Lin Zhu, Joey Bai, Shiqi Wu and Qi He


The main mission of LinkedIn is to connect 610M+ members to the right opportunities. To find the right opportunities, LinkedIn needs to understand each member’s skill set and their expertise levels accurately. However, estimating members’ skill expertise is challenging due to lack of ground-truth. So far, the industry relied on either hand-created small scale data, or large scale social gestures containing a lot of social bias (e.g., endorsements).

In this paper, we develop the Social Skill Validation, a novel framework of collecting validations for members’ skill expertise at the scale of billions of member-skill pairs. Unlike social gestures, we collect signals in an anonymous way to ensure objectiveness. We also develop a machine learning model to make smart suggestions to collect validations more efficiently.

With the social skill validation data, we discover the insights on how people evaluate other people in professional social networks. For example, we find that the members with higher seniority do not necessarily get positive evaluations compared to more junior members. We evaluate the value of social skill validation data on predicting who is hired for a job requiring a certain skill, and model using social skill validation outperforms the state-of-the art methods on skill expertise estimation by 10%. Our experiments show that the Social Skill Validation we built provides a novel way to estimate the members’ skill expertise accurately at large scale and offers a benchmark to validate social theories on peer evaluation.

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