Quality assurance is one the most important challenges in crowdsourcing. Assigning tasks to several workers to increase quality through redundant answers can be expensive if asking homogeneous sources. This limitation has been overlooked by current crowdsourcing platforms resulting this way in costly solutions. In order to achieve desirable cost-quality tradeoffs it is essential to apply efficient crowd access optimization techniques. This work argues that optimization needs to be aware of diversity and correlation of information within groups of individuals so that crowdsourcing redundancy can be adequately planned beforehand. Based on this intuitive idea, we introduce the Access Path Model (APM), a novel crowd model that leverages the notion of access paths as an alternative way of retrieving information. APM aggregates answers ensuring high quality and meaningful confidence. Moreover, we devise a greedy optimization algorithm for this model that finds a provably good approximate plan to access the crowd. We evaluate our approach on three crowdsourced datasets that illustrate various aspects of the problem. Our results show that the Access Path Model combined with greedy optimization is cost-efficient and practical to overcome common difficulties in large-scale crowdsourcing like data sparsity and anonymity.

在众包领域，为提高质量的方法之一是将任务分配给多个工人以通过冗余答案来提高质量，但如果询问的是同质源，则成本可能很高。为了取得良好的成本-质量权衡，应用有效的众包访问优化技术至关重要。我们提出的新型众包模型Access Path Model（APM）充分考虑了信息多样性和相关性，可以提前适当规划众包冗余。我们基于该模型提出了一种贪心优化算法，以便获取高质量且有意义的信心。在三个数据集上的实验证明，该模型与贪心优化结合是经济实惠、能克服大规模众包常见困难的实用方法，如数据稀缺和匿名性。

众包通道优化：多样性至关重要