Finding teams that balance expert load and task coverage
Sofia Maria Nikolakaki, Mingxiang Cai, Evimaria Terzi

TL;DR
This paper introduces the BalancedTA problem, a new team formation approach that balances expert workload and partial task skill coverage, addressing real-world scenarios where full skill coverage isn't always necessary.
Contribution
It formulates the BalancedTA problem, proves its NP-hardness, and develops efficient heuristics, validated on real online labor market datasets.
Findings
Heuristics effectively solve the BalancedTA problem in practice.
The framework improves team formation by balancing skill coverage and workload.
Experimental results demonstrate practical utility on real datasets.
Abstract
The rise of online labor markets (e.g., Freelancer, Guru and Upwork) has ignited a lot of research on team formation, where experts acquiring different skills form teams to complete tasks. The core idea in this line of work has been the strict requirement that the team of experts assigned to complete a given task should contain a superset of the skills required by the task. However, in many applications the required skills are often a wishlist of the entity that posts the task and not all of the skills are absolutely necessary. Thus, in our setting we relax the complete coverage requirement and we allow for tasks to be partially covered by the formed teams, assuming that the quality of task completion is proportional to the fraction of covered skills per task. At the same time, we assume that when multiple tasks need to be performed, the less the load of an expert the better the…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Auction Theory and Applications · Optimization and Search Problems
