Reputation Deflation Through Dynamic Expertise Assessment in Online Labor Markets

Current reputation systems in online labor markets (e.g., Freelancer, PeoplePerHour) experience three major shortcomings: (1) reputation inflation (i.e., reputation scores are inflated to "above average" values) (2) reputation attribution (i.e., attribution of reputation scores to individual skills is unfeasible) and (3) reputation staticity (i.e., reputation scores are uniformly averaged over time). These shortcomings render online reputation systems uninformative, and sometimes even misleading. This work proposes a data-driven approach that deflates reputation scores by solving the problems of reputation attribution and saticity. The deflating process starts with projecting any random set of skills to a set of competency dimensions. For each competency dimension, a Hidden Markov Model estimates a contractor's current (bat latent) competency-specific expertise. Aggregation of competency-specific estimates provides expertise predictions for my given set of required skills. Empirical analysis on 61,330 completed tasks from a major online labor market shows that the resulting estimates arc deflated and they better predict contractor performance. These results suggest a series of implications for online (labor) markets and their users.