图 书 馆
BoltzRank：学习最大化预期排名增益BoltzRank: Learning to Maximize Expected Ranking Gain |
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| 课程网址: | http://videolectures.net/icml09_volkovs_blmerg/ |
| 主讲教师: | Maksims Volkovs |
| 开课单位: | 多伦多大学 |
| 开课时间: | 2009-08-26 |
| 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 query is a popular problem in information retrieval. Methods that learn ranking functions are difficult to optimize, as ranking performance is typically judged by metrics that are not smooth. In this paper we propose a new listwise approach to learning to rank. Our method creates a conditional probability distribution over rankings assigned to documents for a given query, which allows for gradient ascent optimization of the expected value of some performance measure. The rank probabilities take the form of a Boltzmann distribution, based on an energy function that depends on a scoring function composed of individual and pairwise potentials. Including pairwise potentials is a novel contribution, allowing the model to encode regularities in the relative scores of documents; existing models assign scores at test time based only on individual documents, with no pairwise constraints between documents. Experimental results on the LETOR3.0 data sets show that our method out-performs existing learning approaches to ranking. |
| 关 键 词: | 信息检索; 排名函数; 概率分布 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-24：lxf |
| 阅读次数: | 140 |