Incorporating Worker Similarity for Label Aggregation in Crowdsourcing

For the quality control in the crowdsourcing tasks, requesters usually assign a task to multiple workers to obtain redundant answers and then aggregate them to obtain the more reliable answer. Because of the existence of the non-experts in the crowds, one of the problems in the label aggregation is how to differ experts with higher ability from non-experts with lower ability and strengthen the influences of these experts. Most of the existing label aggregation approaches tend to strengthen the workers who provide majority answers and regard them with high ability. In addition, we find that the similarity among worker labels is possible to be effective for this issue because two experts are more probable to reach consensus than two non-experts. We thus propose a novel probabilistic model which can incorporate the similarity information of workers. The experimental results on a number of real datasets show that our approach can outperform the existing models including a probabilistic model without incorporating the similarity. We also make an empirical study on the influence of worker ability, label sparsity and redundancy to the performance of label aggregation approaches, and provide a suggestion on the strategy of collecting the labels in crowdsourcing.