• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International conference on world wide web >Community-Based Bayesian Aggregation Models for Crowdsourcing
【24h】

Community-Based Bayesian Aggregation Models for Crowdsourcing

机译：基于社区的众包贝叶斯聚合模型

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper addresses the problem of extracting accurate labels from crowdsourced datasets, a key challenge in crowdsourcing. Prior work has focused on modeling the reliability of individual workers, for instance, by way of confusion matrices, and using these latent traits to estimate the true labels more accurately. However, this strategy becomes ineffective when there are too few labels per worker to reliably estimate their quality. To mitigate this issue, we propose a novel community-based Bayesian label aggregation model, CommunityBCC, which assumes that crowd workers conform to a few different types, where each type represents a group of workers with similar confusion matrices. We assume that each worker belongs to a certain community, where the worker's confusion matrix is similar to (a perturbation of) the community's confusion matrix. Our model can then learn a set of key latent features: (ⅰ) the confusion matrix of each community, (ⅱ) the community membership of each user, and (ⅲ) the aggregated label of each item. We compare the performance of our model against established aggregation methods on a number of large-scale, real-world crowdsourcing datasets. Our experimental results show that our CommunityBCC model consistently outperforms state-of-the-art label aggregation methods, gaining, on average, 8% more accuracy with the same amount of labels.
机译：本文解决了从众包数据集中提取准确标签的问题，这是众包的关键挑战。先前的工作集中在对单个工人的可靠性进行建模（例如，通过混淆矩阵），并使用这些潜在特征更准确地估计真实标签。但是，当每个工人的标签太少而无法可靠地估计其质量时，此策略将失效。为了缓解此问题，我们提出了一种新颖的基于社区的贝叶斯标签聚集模型CommunityBCC，该模型假定人群工人符合几种不同类型，其中每种类型代表一组具有相似混淆矩阵的工人。我们假设每个工人都属于某个社区，在该社区中，工人的困惑矩阵类似于（对社区的困惑）扰动。然后，我们的模型可以学习一组关键的潜在特征：（ⅰ）每个社区的混淆矩阵，（ⅱ）每个用户的社区成员身份，以及（ⅲ）每个项目的汇总标签。我们将模型的性能与已建立的汇总方法在许多大规模的，现实世界中的众包数据集上进行比较。我们的实验结果表明，我们的CommunityBCC模型始终优于最新的标签聚合方法，在相同数量的标签下，平均精度提高了8％。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号