WorkerRank: Using Employer Implicit Judgements to Infer Worker Reputation

Abstract

In online labor marketplaces two parties are involved; employers and workers. An employer posts a job in the marketplace to receive applications from interested workers. After evaluating the match to the job, the employer hires one (or more workers) to accomplish the job via an online contract. At the end of the contract, the employer can provide his worker with some rating that becomes visible in the worker online profile. This form of explicit feedback guides future hiring decisions, since it is indicative of worker true ability. In this paper, first we discuss some of the shortcomings of the existing reputation systems that are based on the end-of-contract ratings. Then we propose a new reputation mechanism that uses Bayesian updates to combine employer implicit feedback signals in a link-analysis approach. The new system addresses the shortcomings of existing approaches, while yielding better signal for the worker quality towards hiring decision.