Abstract: Crowdsourcing is an increasingly important area of computer applications,because it can address problems that difficult for computer to handle alone.For the openness of crowdsourcing,quality control becomes one of the important challenges.In order to ensure the effectiveness of truth inference,current researches leverage answers of trustful workers to infer truths by evalua-ting worker quality generally.However,most existing methods ignore the long-tail phenomena in crowdsourcing,and there is a lack of researches on the truth inference when the number of tasks completed by workers is generally small.Considering the characteristics of different task types,long-tail phenomenon and worker answers,this paper constructs the confidence interval of small samples to solve truth inference when the number of tasks completed by workers are generally small.Firstly,worker quality is pre-estimated according to the gold standard answer strategy,and different truth initialization methods are adopted according to the result of pre-estimated.Then,the confidence interval of small samples is constructed to evaluate worker quality accurately.Finally,task truths are inferred and worker quality is updated iteratively.In order to verify the effectiveness of the proposed me-thod,5 real datasets are selected to conduct experiments.Compared with the existing methods,the proposed method can solve the problem of the long tail phenomenon effectively,especially the number of tasks completed by each worker is generally small.The average accuracy of the proposed method for the single-choice tasks is as high as 93%,and higher than 16% of the bestperfor-mance of the existing methods.Meanwhile,the values of MAE and RMSE of the proposed method for the numerical tasks are lower than that of the existing methods.

Key words: Crowdsourcing, Long-tail phenomenon, Small sample confidence interval, Truth inference, Worker quality estimation