A neural-based bandit approach to mobile crowdsourcing
A neural-based bandit approach to mobile crowdsourcing
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DOI：
10.1145/3508396.3512886
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发表时间：
2022-03
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影响因子：
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通讯作者：
Shouxu Lin;Yuhang Yao;Pei Zhang;Hae Young Noh;Carlee Joe-Wong
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文献类型：
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作者：
Shouxu Lin;Yuhang Yao;Pei Zhang;Hae Young Noh;Carlee Joe-Wong
移动的众包长期以来一直承诺利用移动的群体的力量来减少执行大规模位置相关任务所需的时间和金钱成本，环境传感然而，将正确的任务分配给正确的用户是一个长期的挑战：不同的用户将更适合不同的任务，这反过来又会对整个众包目标做出不同的贡献。更糟糕的是，这些关系通常是先验未知的，并且可能随时间而改变，特别是在移动的设置中。物联网中设备的多样性以及它们可能运行的新应用任务的多样性加剧了这些挑战。因此，在本文中，我们制定的移动的众包问题作为一个上下文组合挥发性多武装土匪问题。尽管先前的工作已经尝试基于用户特定的辅助信息来学习最优的用户任务分配，但是这样的公式化假设上下文信息、用户对每个任务的适合性和总体众包目标之间的关系中的已知结构。为了放松这些假设，我们提出了一种可以学习这些关系的Neural-MAB算法。我们表明，在一个模拟的移动的众包应用程序中，我们的算法显着优于现有的多臂强盗基线设置与已知和未知的奖励结构。
Mobile crowdsourcing has long promised to utilize the power of mobile crowds to reduce the time and monetary cost required to perform large-scale location-dependent tasks, e.g., environmental sensing. Assigning the right tasks to the right users, however, is a longstanding challenge: different users will be better suited for different tasks, which in turn will have different contributions to the overall crowdsourcing goal. Even worse, these relationships are generally unknown a priori and may change over time, particularly in mobile settings. The diversity of devices in the Internet of Things and diversity of new application tasks that they may run exacerbate these challenges. Thus, in this paper, we formulate the mobile crowdsourcing problem as a Contextual Combinatorial Volatile Multi-armed Bandit problem. Although prior work has attempted to learn the optimal user-task assignment based on user-specific side information, such formulations assume known structure in the relationships between contextual information, user suitability for each task, and the overall crowdsourcing goal. To relax these assumptions, we propose a Neural-MAB algorithm that can learn these relationships. We show that in a simulated mobile crowdsourcing application, our algorithm significantly outperforms existing multi-armed bandit baselines in settings with both known and unknown reward structures.