IEEE Trans Cybern. 2017 Dec;47(12):4208-4222. doi: 10.1109/TCYB.2016.2602498. Epub 2016 Sep 7.
Crowdsourcing has become a popular service computing paradigm for requesters to integrate the ubiquitous human-intelligence services for tasks that are difficult for computers but trivial for humans. This paper focuses on crowdsourcing complex tasks by team formation in social networks (SNs) where a requester connects to a large number of workers. A good indicator of efficient team collaboration is the social connection among workers. Most previous social team formation approaches, however, either assume that the requester can maintain information of all workers and can directly communicate with them to build teams, or assume that the workers are cooperative and be willing to join the specific team built by the requester, both of which are impractical in many real situations. To this end, this paper first models each worker as a selfish entity, where the requester prefers to hire inexpensive workers that require less payment and workers prefer to join the profitable teams where they can gain high revenue. Within the noncooperative SNs, a distributed negotiation-based team formation mechanism is designed for the requester to decide which worker to hire and for the worker to decide which team to join and how much should be paid for his skill service provision. The proposed social team formation approach can always build collaborative teams by allowing team members to form a connected graph such that they can work together efficiently. Finally, we conduct a set of experiments on real dataset of workers to evaluate the effectiveness of our approach. The experimental results show that our approach can: 1) preserve considerable social welfare by comparing the benchmark centralized approaches and 2) form the profitable teams within less negotiation time by comparing the traditional distributed approaches, making our approach a more economic option for real-world applications.
众包已成为一种流行的服务计算范例，请求者可以整合无处不在的人类智能服务，以完成对计算机来说困难但对人类来说微不足道的任务。本文专注于通过社交网络 (SN) 中的团队形成来众包复杂任务，请求者在社交网络中连接到大量的工人。有效的团队协作的一个良好指标是工人之间的社会联系。然而，大多数先前的社交团队形成方法要么假设请求者可以维护所有工人的信息，并可以直接与他们沟通以组建团队，要么假设工人是合作的，并愿意加入请求者组建的特定团队，这在许多实际情况下都是不切实际的。为此，本文首先将每个工人建模为一个自私的实体，请求者更愿意雇佣需要较少支付的廉价工人，而工人更愿意加入利润丰厚的团队，在这些团队中他们可以获得高收入。在非合作的 SN 中，设计了一种基于分布式协商的团队形成机制，供请求者决定雇佣哪个工人，以及工人决定加入哪个团队以及应该为他的技能服务支付多少费用。所提出的社交团队形成方法可以通过允许团队成员形成一个连接的图来始终建立协作团队，以便他们可以高效地合作。最后，我们在工人的真实数据集上进行了一组实验，以评估我们的方法的有效性。实验结果表明，我们的方法可以：1）通过与基准集中式方法进行比较来保留相当大的社会福利，以及 2）通过与传统分布式方法进行比较来在更少的协商时间内形成有利可图的团队，从而使我们的方法成为更具经济意义的现实应用选择。
