Crowd Trust: A context-aware trust model for worker selection in crowdsourcing environments

On a crowd sourcing platform consisting of task publishers and workers, it is critical for a task publisher to select trustworthy workers to solve human intelligence tasks (HITs). Currently, the prevalent trust evaluation mechanism employs the overall approval rate of HITs, with which dishonest workers can easily succeed in pursuing the maximal profit by quickly giving plausible answers or counterfeiting HITs approval rates. In crowd sourcing environments, a worker's trustworthiness varies in contexts, i.e. It varies in different types of tasks and different reward amounts of tasks. Thus, we propose two classifications based on task types and task reward amount respectively. On the basis of the classifications, we propose a trust evaluation model, which consists of two types of context-aware trust: task type based trust (Tat rust) and reward amount based trust (Rat rust). Then, we model trustworthy worker selection as a multi-objective combinatorial optimization problem, which is NP-hard. For solving this challenging problem, we propose an evolutionary algorithm MOWS-GA based on NSGA-II. The results of experiments illustrate that our proposed trust evaluation model can effectively differentiate honest workers and dishonest workers when both of them have high overall HITs approval rates.