Analytic Methods for Optimizing Realtime Crowdsourcing
Analytic Methods for Optimizing Realtime Crowdsourcing
复制标题
优化实时众包的分析方法
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Joel Brandt
中科院分区：
文献类型：
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作者：
Michael S. Bernstein;David R Karger;Rob Miller;Joel Brandt
实时众包研究表明，通过管理一个反应迅速的小型员工池，有可能在几秒钟内招募到付费人群。实时人群使由人群提供动力的系统能够以交互速度做出反应：例如，相机、机器人和即时民意调查。到目前为止，这些技术主要是概念验证原型：研究尚未尝试了解它们如何大规模工作或优化其成本/性能权衡。本文利用排队论分析了实时众包的定员模型，特别是它对请求者的期望等待时间和费用。我们提供了一种算法，允许请求者在满足性能要求的情况下将成本降至最低。然后，我们提出并分析了三种提高性能的技术：推送通知、共享保留器池和Preruitment，后者涉及在任务实际到达之前召回保留程序工作人员。一项实验验证发现，有经验的员工在任务发布后500毫秒就开始工作，交付的结果低于最终用户保持流畅的一秒认知阈值。
Realtime crowdsourcing research has demonstrated that it is possible to recruit paid crowds within seconds by managing a small, fast-reacting worker pool. Realtime crowds enable crowd-powered systems that respond at interactive speeds: for example, cameras, robots and instant opinion polls. So far, these techniques have mainly been proof-of-concept prototypes: research has not yet attempted to understand how they might work at large scale or optimize their cost/performance trade-offs. In this paper, we use queueing theory to analyze the retainer model for realtime crowdsourcing, in particular its expected wait time and cost to requesters. We provide an algorithm that allows requesters to minimize their cost subject to performance requirements. We then propose and analyze three techniques to improve performance: push notifications, shared retainer pools, and precruitment, which involves recalling retainer workers before a task actually arrives. An experimental validation finds that precruited workers begin a task 500 milliseconds after it is posted, delivering results below the one-second cognitive threshold for an end-user to stay in flow.