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A Long-Term Quality Perception Incentive Strategy for Crowdsourcing Environments with Budget Constraints
International Journal of Cooperative Information Systems ( IF 1.5 ) Pub Date : 2020-01-31 , DOI: 10.1142/s0218843020400055
Li-Ping Gao 1, 2, 3 , Tao Jin 1 , Chao Lu 3
Affiliation  

Quality control is a critical design goal for crowdsourcing. However, when measuring the long-term quality of workers, the existing strategies do not make effective use of workers’ historical information, whereas others regard workers’ conditions as fixed values, even if they do not consider the impact of workers’ quality. This paper proposes a long-term quality perception incentive model (called QAI model) in a crowdsourcing environment with budget constraints. In this work, QAI divides the entire long-term activity cycle into multiple stages based on proportional allocation rules. Each stage treats the interaction between the requester and the worker as a reverse auction process. At each stage, a truthful, individually rational, budget feasible, quality-aware task allocation algorithm is designed. At the end of each stage, according to hidden Markov model (HMM), this paper proposes a new framework for quality prediction and parameter learning framework, which can make use of workers’ historical information efficiently. Experiments have verified the feasibility of our algorithm and showed that the proposed QAI model leads to improved results.

中文翻译:

具有预算约束的众包环境的长期质量认知激励策略

质量控制是众包的一个关键设计目标。然而,在衡量工人的长期素质时,现有的策略并没有有效地利用工人的历史信息,而另一些则将工人的状况视为固定值,即使他们没有考虑工人素质的影响。本文提出了一种在预算约束的众包环境下的长期质量感知激励模型(称为 QAI 模型)。在这项工作中,QAI 根据比例分配规则将整个长期活动周期分为多个阶段。每个阶段都将请求者和工作人员之间的交互视为反向拍卖过程。在每个阶段,都设计了一个真实的、个体合理的、预算可行的、有质量意识的任务分配算法。在每个阶段结束时,根据隐马尔可夫模型(HMM),本文提出了一种新的质量预测框架和参数学习框架,可以有效地利用工人的历史信息。实验验证了我们算法的可行性,并表明所提出的 QAI 模型导致了改进的结果。
更新日期:2020-01-31
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