标题： 基于影响力和兴趣的在线社交网络众包工作者招聘 显示英文标题

标题： Influence- and Interest-based Worker Recruitment in Crowdsourcing using Online Social Networks

摘要： 工人招募仍然是移动众包（MCS）中的一个重要问题，其目标是招募一组能够最大化预期服务质量（QoS）的工人。当前的招募系统假设有一个预定义的工人池可用。然而，这种假设并不总是成立，尤其是在冷启动情况下，当一个新的MCS任务刚刚发布时。此外，研究表明，多达96%的可用候选人通常不愿意执行分配的任务。为了解决这些问题，最近的研究使用在线社交网络（OSN）和影响最大化（IM）通过影响者来宣传所需的MCS任务，旨在建立更大的工人池。然而，这些研究存在一些局限性，例如1）在选择影响者时缺乏基于群体的选择方法，2）在IM之后缺乏明确的工人招募流程，3）招募过程缺乏动态性，即拒绝执行任务的工人没有被替换。本文提出了一种基于影响和兴趣的工人招募系统（IIWRS），使用OSN。该系统有两个主要组件：1）一种基于MCS、群体和兴趣的影响最大化方法，使用遗传算法从网络中选择一组影响者来宣传MCS任务，2）一个动态的工人招募过程，考虑工人的社会属性，并能够替换那些不接受执行分配任务的工人。使用真实数据集进行了实证研究，并将IIWRS与现有基准进行比较。 显示英文摘要

摘要： Workers recruitment remains a significant issue in Mobile Crowdsourcing (MCS), where the aim is to recruit a group of workers that maximizes the expected Quality of Service (QoS). Current recruitment systems assume that a pre-defined pool of workers is available. However, this assumption is not always true, especially in cold-start situations, where a new MCS task has just been released. Additionally, studies show that up to 96\% of the available candidates are usually not willing to perform the assigned tasks. To tackle these issues, recent works use Online Social Networks (OSNs) and Influence Maximization (IM) to advertise about the desired MCS tasks through influencers, aiming to build larger pools. However, these works suffer from several limitations, such as 1) the lack of group-based selection methods when choosing influencers, 2) the lack of a well-defined worker recruitment process following IM, 3) and the non-dynamicity of the recruitment process, where the workers who refuse to perform the task are not substituted. In this paper, an Influence- and Interest-based Worker Recruitment System (IIWRS), using OSNs, is proposed. The proposed system has two main components: 1) an MCS-, group-, and interest-based IM approach, using a Genetic Algorithm, to select a set of influencers from the network to advertise about the MCS tasks, and 2) a dynamic worker recruitment process which considers the social attributes of workers, and is able to substitute those who do not accept to perform the assigned tasks. Empirical studies are performed using real-life datasets, while comparing IIWRS with existing benchmarks.