图 书 馆
带列表和成对约束的区间秩等渗回归IntervalRank - Isotonic Regression with Listwise and Pairwise Constraints |
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| 课程网址: | http://videolectures.net/wsdm2010_moon_irir/ |
| 主讲教师: | Taesup Moon |
| 开课单位: | 雅虎公司 |
| 开课时间: | 2010-09-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Ranking a set of retrieved documents according to their relevance to a given query has become a popular problem at the intersection of web search, machine learning, and information retrieval. Recent work on ranking focused on a number of different paradigms, namely, pointwise, pairwise, and list-wise approaches. Each of those paradigms focuses on a different aspect of the dataset while largely ignoring others. The current paper shows how a combination of them can lead to improved ranking performance and, moreover, how it can be implemented in log-linear time. The basic idea of the algorithm is to use isotonic regression with adaptive bandwidth selection per relevance grade. This results in an implicitly-defined loss function which can be minimized efficiently by a subgradient descent procedure. Experimental results show that the resulting algorithm is competitive on both commercial search engine data and publicly available LETOR data sets. |
| 关 键 词: | 网络搜索; 机器学习; 信息检索 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-01-13：chenxin |
| 阅读次数: | 87 |