Efficient scheduling in crowdsourcing based on workers' mood
Efficient scheduling in crowdsourcing based on workers' mood
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DOI：
10.1109/agents.2017.8015317
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Han Yu;Zhiqi Shen;Simon Fauvel;Li-zhen Cui
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文献类型：
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作者：
Han Yu;Zhiqi Shen;Simon Fauvel;Li-zhen Cui
众包工作者需要合理安排何时工作、何时休息，以保持良好的生产力并保证众包系统的长期运行。然而，现有的算法众包工作很大程度上忽视了这个问题，只关注优化工人之间任务分配的策略。在本文中，我们提出了情感众包（AC）方法来填补这一重要空白。它是一种分布式动态调度方法，可最大限度地减少众包工人的工作支出，同时实现高集体生产力。我们的方法利用有关人们情绪与生产力之间关系的新兴证据来实施劳动力管理研究中的“生产性懒惰”概念。基于天池发布的大规模数据集的大量实验研究表明，与其他替代调度方法相比，AC 显着节省了工人的工作量，同时在任务完成率方面的牺牲最小。所提出的方法很新颖，为众包工人建立了一个框架来优化他们的工作和休息时间表。
Crowdsourcing workers need to properly schedule when to work and when to rest in order maintain good productivity and ensure the long-term operation of crowdsourcing systems. However, existing algorithmic crowdsourcing works largely overlook this problem and only focus on strategies to optimize the distribution of tasks among workers. In this paper, we propose the Affective Crowdsourcing (AC) approach to fill this important gap. It is a distributed dynamic scheduling approach which minimizes crowdsourcing worker effort expenditure while achieving high collective productivity. Our approach leverages the emerging body of evidence about the relationship between people's mood and their productivity to operationalize the ‘productive laziness’ concept from workforce management research. Extensive experimental studies based on a large-scale dataset released by Tianchi demonstrate that AC significantly conserves worker effort, while making the smallest sacrifice in terms of task completion rates as compared to other alternative scheduling approaches. The proposed approach is novel and establishes a framework for crowdsourcing workers to optimize their work and rest schedules.