Enhancing User Experience of Task Assignment in Spatial Crowdsourcing: A Self-Adaptive Batching Approach
Enhancing User Experience of Task Assignment in Spatial Crowdsourcing: A Self-Adaptive Batching Approach
复制标题
增强空间众包中任务分配的用户体验：一种自适应批处理方法
DOI：
10.1109/access.2019.2940028
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Liu, An
中科院分区：
文献类型：
--
作者：
Qian, Lai;Liu, Guanfeng;Liu, An
面对现实世界应用的爆发式需求，空间众包受到了广泛关注，在过去几年中，任务分配算法在其中占据主导地位。一方面，近期的大多数研究集中于最大化平台的整体效益，却忽视了用户体验在任务分配中同样起着至关重要的作用这一事实。另一方面，这些研究侧重于匹配，即如何分配任务，而非批处理，即何时进行任务分配。实际上，用户体验也取决于批处理，但这一点在当前的研究中在很大程度上被忽视了。在本文中，我们提出了一种自适应批处理机制，以提升空间众包中的用户体验。通过合适的启动时间戳，以往的匹配方法能够取得更好的效果。我们采用强化学习中的多臂老虎机算法，根据历史当前状态动态地划分批次。在真实数据集和合成数据集上进行的大量实验结果证明了所提方法的有效性和高效性。
Faced with the explosive demand of real-world applications, spatial crowdsourcing has attracted much attention, in which task assignment algorithms take the dominant role in the past few years. On the one hand, most recent studies concentrate on maximizing the overall benefits of the platform, ignoring the fact that user experience also plays an essential role in task allocation. On the other hand, they focus on matching, that is, how to assign tasks, rather than batching, that is, when to make assignment. In fact, user experience also depends on batching, but this is largely overlooked by current studies. In this paper, we propose a self-adaptive batching mechanism to enhance user experience in spatial crowdsourcing. With appropriate start-up timestamps, previous matching methods can perform better. Multi-armed bandit algorithm in reinforcement learning is adopted to split the batch dynamically according to historical current states. Extensive experimental results on both real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed approach.