CAT: Context-Aware Trust-Oriented Worker Selection in Social Crowd

Abstract

Finding trustworthy worker is a longstanding issue in crowdsourcing systems. On traditional crowdsouring platforms, like Amazon Mechanical Turk\reffn:Amazon Mechanical Turk, the trustworthiness of a worker is usually based on the contextual information, like different types of tasks and different reward amounts of tasks. However, with the combination of OSNs and the crowdsourcing applications in social crowd, in addition to the above mentioned task based contexts, the social contexts like the social relationships and the social positions of participants can greatly assist requestors to select trustworthy workers. In order to select the trustworthy workers in social crowd, in this paper, we first present a contextual social network structure which contains complex social contexts. Then we propose a trust evaluation model taking both contexts information and the requirements of requestors into consideration, which leads to the trust worker selection in social crowd as a classical NP-Complete Multi-Constrained Optimal Path (MCOP) selection problem. For solving this challenging problem, we propose a new efficient and effective approximation Context-Aware Trust-Oriented Worker Selection algorithm CAT. The results of our experiments conducted on four real OSN datasets illustrate the superiority of our method in trustworthy worker selection.