Cross-Platform Online Team Formation in Spatial Crowdsourcing

Spatial crowdsourcing has become popular in recent years, but traditional tasks focus on one-to-one services with single skills like food delivery and ride-hailing. As societal needs grow more complex, there is a need for tasks requiring teams with multiple skills. Current team formation methods using workers from a single platform limit skill diversity, leading to potential task delays, lower quality, and revenue losses. Although cross-platform cooperation offers a potential solution to skill diversity limitations, it faces two challenges: (1) Data protection regulations mandate that platform's raw data must remain localized; (2) cross-platform cooperation incurs additional cooperation costs. To address these challenges, we first define the Cross-platform Online Team Formation (COTF) problem. We then propose a COTF framework and Random Cooperation Strategy to solve COTF problem. To enhance the effectiveness of cooperation, we further propose Precision Query Range Optimization Strategy (PQROS) for worker selection through adaptive range queries, and Dynamic Query Optimization (DQO) for cost-effective scheduling via predictive revenue modeling. Extensive experiments on real and synthetic datasets validate the effectiveness of our proposed methods.