Analysis and Prediction of Endorsement-Based Skill Assessment in LinkedIn

In LinkedIn, skill endorsements are used as a key indicator to assess a user's skill level so that a recruiter can find right candidates. However, the simple counting of skill endorsements has shown serious limitations to correctly assess a user's skill level particularly when false endorsements prevail for self-promotion or collusive promotion. To address this issue, we propose a framework using regression analysis to evaluate assessment methods under varying the degree of false endorsements. We define two types of assessment scores: authority score (AS) and credit score (CS). The AS is calculated based on an endorser's expertise level in a particular skill set while the CS is estimated based on the endorser's trustworthiness. Based on these two types of skill assessments, we define a hybrid method that calculates AS based on CS, which is called credit-based AS, or namely CAS. We conduct performance analysis by comparing the AS and CAS with the endorsement count (EC) as a baseline model, which is currently used in LinkedIn. Through the extensive regression analysis, under varying the degree of false endorsements from 10% to 25%, we observe that the CAS outperforms AS and EC with less than 10% of false endorsements. On the other hand, the AS performs the best among three methods with 10%-25% of false endorsements. Based on the regression analysis of the skillset assessment framework, the real dataset collected for this study is likely to have 4%-6% of false endorsements. This finding implies that an endorser's expertise appears more critical than the endorser's trustworthiness under more presence of false endorsements because the unqualified endorser's endorsement may not be necessarily true.