Maximizing user type diversity for task assignment in crowdsourcing
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Maximizing user type diversity for task assignment in crowdsourcing Ana Wang1,2,3 · Meirui Ren3 · Hailong Ma3 · Lichen Zhang1,2,3 · Peng Li1,2,3 · Longjiang Guo1,2,3 Published online: 3 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
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.
Ana Wang and Meirui Ren have contributed equally to this work.
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Lichen Zhang [email protected] Longjiang Guo [email protected] Ana Wang [email protected] Meirui Ren [email protected] Hailong Ma [email protected] Peng Li [email protected]
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Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an 710062, China
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Engineering Laboratory of Teaching Information Technology of Shaanxi Province, Xi’an 710119, China
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School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
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Journal of Combinatorial Optimization (2020) 40:1092–1120
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Keywords Crowdsourcing · Crowdsensing · Task assignment · User type diversity
1 Introduction Crowdsourcing (Xing et al. 2019; Qiao et al. 2018; Li et al. 2018c) mostly comprises taking a large task and separating it into many smaller tasks on which a crowd of people can work separately. It involves obtaining work, information, or data from a large group of people who present their data via social communication software and smart phone apps on the Internet. In the existing research literature, crowdsourcing mainly has the following research directions, namely task assignment (Cai et al. 2020; Wang and Wang 2016; Tong et al. 2019), incentive mechanism (Wang et al. 2020) and privacy protection (Liu et al. 2020). This paper focuses on how to maximize user type diversity. For example, mobile crowdsensing (MCS) (Ganti et al. 2011; Wei et al. 2018; Zhu et al. 2019; Duan et al. 2019) is a kind of crowdsourcing. It is a new paradigm and widely applied in many areas, which mainly utilizes the sensing, computing, storage and communication functions of mobile smart devices. It regards mobile smart devices carried by mobile users as wireless sensors with powerful functions and completes the sensing task together through their cooperation. In M
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