图 书 馆
用于强盗调查问题的自适应众包算法Adaptive Crowdsourcing Algorithms for the Bandit Survey Problem |
|
| 课程网址: | http://videolectures.net/colt2013_slivkins_problem/ |
| 主讲教师: | Aleksandrs Slivkins |
| 开课单位: | 微软公司 |
| 开课时间: | 2013-08-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Very recently crowdsourcing has become the de facto platform for distributing and collecting human computation for a wide range of tasks and applications such as information retrieval, natural language processing and machine learning. Current crowdsourcing platforms have some limitations in the area of quality control. Most of the effort to ensure good quality has to be done by the experimenter who has to manage the number of workers needed to reach good results.We propose a simple model for adaptive quality control in crowdsourced multiple-choice tasks which we call the “bandit survey problem”. This model is related to, but technically different from the well-known multi-armed bandit problem. We present several algorithms for this problem, and support them with analysis and simulations.Our approach is based in our experience conducting relevance evaluation for a large commercial search engine. |
| 关 键 词: | 众包; 强盗调查; 搜索引擎 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-08：吴雨秋(课程编辑志愿者) |
| 阅读次数: | 116 |