Multi-team Formation System for Collaborative Crowdsourcing
Ryota Yamamoto, Kazushi Okamoto

TL;DR
This paper introduces a heuristic algorithm for forming optimal multi-team groups in crowdsourcing by considering worker compatibility, expertise, and budget constraints, utilizing social network data and simulation-based analysis.
Contribution
It presents a novel social network-based heuristic optimization method for multi-team formation in crowdsourcing, outperforming traditional hill-climbing approaches.
Findings
Algorithm outperforms hill-climbing in most conditions
Optimal parameters include a temperature decrease rate of about 0.9
Low team size, social network degree, and budget lead to poor evaluation values
Abstract
For complex crowdsourcing tasks that require collaboration between multiple individuals, teams should be formed by considering both worker compatibility and expertise. Furthermore, the nature of crowdsourcing dictates the budget for tasks and workers' remuneration, and excessively large team sizes may reduce collaborative performance. To address these challenges, we propose a heuristic optimization algorithm that leverages social network information to simultaneously form teams with optimized worker compatibility for multiple tasks. In our approach, historical collaboration is represented as a social network, where the edge weights correspond to explicit ratings of worker compatibility. In a simulation experiment using synthetic data, we applied Gaussian process regression to examine the relationship between eight experimental parameters and evaluation values, thereby analyzing the…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Tactile and Sensory Interactions · Open Source Software Innovations
