Maximizing user type diversity for task assignment in crowdsourcing

Ana Wang (), Meirui Ren (), Hailong Ma (), Lichen Zhang (), Peng Li () and Longjiang Guo ()
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Ana Wang: Ministry of Education
Meirui Ren: Shaanxi Normal University
Hailong Ma: Shaanxi Normal University
Lichen Zhang: Ministry of Education
Peng Li: Ministry of Education
Longjiang Guo: Ministry of Education

Journal of Combinatorial Optimization, 2020, vol. 40, issue 4, No 15, 1092-1120

Abstract: Abstract Crowdsourcing employs numerous users to perform certain tasks, in which task assignment is a challenging issue. Existing researches on task assignment mainly consider spatial–temporal diversity and capacity diversity, but not focus on the type diversity of users, which may lead to low quality of tasks. This paper formalizes a novel task assignment problem in crowdsourcing, where a task needs the cooperation of various types of users, and the quality of a task is highly related to the various types of the recruited users. Therefore, the goal of the problem is to maximize the user type diversity subject to limited task budget. This paper uses three heuristic algorithms to try to resolve this problem, so as to maximize user type diversity. Through extensive evaluation, the proposed algorithm Unit Reward-based Greedy Algorithm by Type obviously improves the user type diversity under different user type distributions.

Keywords: Crowdsourcing; Crowdsensing; Task assignment; User type diversity (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10878-020-00645-6

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