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Abstract

The recent surge in popularity of crowdsourcing has brought with it a new opportunity for engaging human intelligence in the process of data analysis. Crowdsourcing provides a fundamental mechanism for enabling online workers to participate in tasks that are either too difficult to be solved solely by a computer or too expensive to employ experts to perform. In the field of social science, four elements are required to form a wise crowd - Diversity of Opinion, Independence, Decentralization and Aggregation. However, while the other three elements are already studied and implemented in current crowdsourcing platforms, the 'Diversity of Opinion' has not been functionally enabled. In this paper, we address the algorithmic optimizations towards the diversity of opinion of crowdsourcing marketplaces. From a computational perspective, in order to build a wise crowd, we need to quantitatively modeling the diversity, and take it into consideration for constructing the crowd. In a crowdsourcing marketplace, we usually encounter two basic paradigms for worker selection: building a crowd to wait for tasks to come and selecting workers for a given task. Therefore, we propose our Similarity-driven Model (S-Model) and Task-driven Model (T-Model) for both of the paradigms. Under both of the models, we propose efficient and effective algorithms to enlist a budgeted number of workers, which have the optimal diversity. We have verified our solutions with extensive experiments on both synthetic datasets and real data sets.