图 书 馆
基于距离模型的无监督秩排序Unsupervised Rank Aggregation with Distance-Based Models |
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| 课程网址: | http://videolectures.net/icml08_klementiev_ura/ |
| 主讲教师: | Alexandre Klementiev |
| 开课单位: | 伊利诺伊大学 |
| 开课时间: | 2008-08-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The need to meaningfully combine sets of rankings often comes up when one deals with ranked data. Although a number of heuristic and supervised learning approaches to rank aggregation exist, they require domain knowledge or supervised ranked data, both of which are expensive to acquire. In order to address these limitations, we propose a mathematical and algorithmic framework for learning to aggregate (partial) rankings without supervision. We instantiate the framework for the cases of combining permutations and combining top-k lists, and propose a novel metric for the latter. Experiments in both scenarios demonstrate the effectiveness of the proposed formalism. |
| 关 键 词: | 排名数据; 形式主义; 监督式学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-13：yumf |
| 阅读次数: | 57 |