A Self-organizing Collaborative Crowdsourcing Framework for Improving Service Utility
Shipeng Wang, Qingzhong Li, Xudong Lü, Lizhen Cui · 2024
Crowdsourcing has been widely adopted in various domains for problem-solving, idea generation and data collection, leveraging distributed networks for efficient outcomes. Crowdsourcing platforms harness workers’ collective productivity by assigning tasks to a diverse pool of workers. However, all these tasks are assigned to registered high-quality workers on the platform, which is prone to task congestion. Moreover, the mechanical scheduling of workers ignores workers’ productivity fluctuations, and fails to make full use of the productivity of high-quality workers who are not currently online. In order to solve these problems, we design a Self-organizing Collaborative Crowdsourcing Framework (SoCCF) to support worker collaboration. Specifically, we propose a two-stage strategy, including a Reputation-driven Collaborative Partner Selection (RCPS) algorithm to expand the pool of collaborative workers and a Lyapunov optimization-based Task Acceptance and Sub-Delegation (LTASD) algorithm to guide worker to make workload decisions that meet emotional needs. Extensive experiments based on simulated crowdsourcing scenarios demonstrate that SoCCF consistently achieves higher overall service utility, while ensuring that workers can achieve 90.2% of the benefits of traditional algorithms with only 82.5% effort on average.