【作者】 赵阳；

【导师】 刘冠峰；

【作者基本信息】 苏州大学 ， 计算机科学与技术， 2017， 硕士

【摘要】 在一些基于社交网络的众包平台上,例如Freelancer和Quora,任务需求者可以在社交网络中找到满足自己需求的众包工人。在这个过程中,不诚信的众包工人往往通过一些典型的欺骗手段,例如夸大个人才能或者伪造个人信誉等,来欺骗现有的信任评估模型,从而获取虚假但很高的信任值。因此,提出有效的信任评估模型是非常紧要的,以此防止这些典型欺骗,探测这些不诚信行为,获取更为准确的信任评估结果,帮助任务需求者查找更为诚信的众包工人,提高任务结果的正确性,减少任务需求者的时间和经济开销。本文首先提出了基于社交背景的复杂社交网络结构以及信任质量的概念。接着将基于社交网络的众包平台中工人的信任评估问题转化成在社交网络中查询源点需求者到目标工人之间的满足多约束条件的社交信任路径的问题,这是一个典型的NP完全问题。为了解决这个问题,基于Monte Carlo算法和四个优化策略,本文提出了一种有效且高效的信任评估算法C-AWSA。此外,为了提高算法的有效性及高效性,本文提出了社交网络强关联单元的概念,并为社交网络强关联单元添加了新颖的索引结构。并且,为了考虑更为全面的众包平台中的任务背景,即任务类型和任务奖赏,本文提出了两种任务分类的方法。基于这两种任务分类方法以及工人的历史任务记录,本文提出了工人信任值的计算方法(TaTrust和RaTrust)。最后,综合考虑社交网络中的社交背景以及众包平台中的任务背景,本文提出了一种更为有效的信任评估算法CAT。CAT算法在C-AWSA算法的基础上进行了优化,对工人的信任评估结果更加准确。在实验部分,我们在真实数据集上验证了本文提出的方法的效果。实验结果表明,本文提出的C-AWSA算法和CAT算法在有效性和高效性上都要优于以往的信任评估算法。更多还原

【Abstract】 In some crowdsourcing platforms based on Online Social Networks(OSNs),like Freelancer and Quora,a requester can find a group of crowd workers that can meet the requirements.During this process,untrustworthy workers can cheat the existing trust evaluation models by using some typical deceptions,like counterfeiting good reputations and overstating personal skills,to obtain fake but high trust values.Therefore,it is necessary and significant to build up an efficient and effective trust evaluation model to defense these deceptions and help deliver accurate trust evaluation results.In this thesis,we firstly propose the contextual social network structure,which has the social contexts like social trust,social relationship and social positions and the concept of Quality of Trust(QoT).Then we model the trust evaluation problem in social crowd as the problem of finding the optimal social trust path with multiple constraints of social contexts,which is the classical NP-Complete Multi-Constrained Optimal Path(MCOP)selection problem.Secondly,to deal with this challenging problem,based on the Monte Carlo method and our optimization search strategies,we propose a new efficient and effective approximation method,Context-Aware Worker Selection Algorithm C-AWSA.Besides,in order to improve the effectiveness and efficiency of our algorithm,we propose a concept of Strong Social Component(SSC)in the social networks,which emblems a group of workers who have strong connections.And we propose a novel index for SSC.Thirdly,as the contextual information in crowdsourcing can also affect the trustworthiness of the worker,we take the task based contexts,i.e.,types of tasks and reward amounts of tasks,into consideration,and we propose two classifications based on task types and task reward amount respectively.On the basis of the classifications and the workers’ historical records,we propose a trust evaluation model,which consists of two types of context-aware trust: task type based trust(TaTrust)and reward amount based trust(RaTrust).Finally,we propose a novel context-aware trust evaluation algorithm CAT,which is more effective than C-AWSA,as CAT considers not only social contexts,but also crowd contexts.We demonstrate the effectiveness and availability of the proposed methods on on real-world datasets.The experimental results show that our proposed C-AWSA and CAT outperform the state-of-the-art trust evaluation methods in effectiveness and efficiency.更多还原