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Learning multiple metrics for ranking

文献类型：期刊论文

作者Geng, Xiubo; Cheng, Xue-Qi
刊名FRONTIERS OF COMPUTER SCIENCE IN CHINA
出版日期2011-09-01
卷号5期号:3页码:259-267
关键词learning to rank multiple measures direct optimization
ISSN号1673-7350
DOI10.1007/s11704-011-0152-5
英文摘要Directly optimizing an information retrieval (IR) metric has become a hot topic in the field of learning to rank. Conventional wisdom believes that it is better to train for the loss function on which will be used for evaluation. But we often observe different results in reality. For example, directly optimizing averaged precision achieves higher performance than directly optimizing precision@3 when the ranking results are evaluated in terms of precision@3. This motivates us to combine multiple metrics in the process of optimizing IR metrics. For simplicity we study learning with two metrics. Since we usually conduct the learning process in a restricted hypothesis space, e.g., linear hypothesis space, it is usually difficult to maximize both metrics at the same time. To tackle this problem, we propose a relaxed approach in this paper. Specifically, we incorporate one metric within the constraint while maximizing the other one. By restricting the feasible hypothesis space, we can get a more robust ranking model. Empirical results on the benchmark data set LETOR show that the relaxed approach is superior to the direct linear combination approach, and also outperforms other baselines.
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000293638400001
出版者HIGHER EDUCATION PRESS
源URL[http://119.78.100.204/handle/2XEOYT63/13233]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Geng, Xiubo
作者单位Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Geng, Xiubo,Cheng, Xue-Qi. Learning multiple metrics for ranking[J]. FRONTIERS OF COMPUTER SCIENCE IN CHINA,2011,5(3):259-267.
APA Geng, Xiubo,&Cheng, Xue-Qi.(2011).Learning multiple metrics for ranking.FRONTIERS OF COMPUTER SCIENCE IN CHINA,5(3),259-267.
MLA Geng, Xiubo,et al."Learning multiple metrics for ranking".FRONTIERS OF COMPUTER SCIENCE IN CHINA 5.3(2011):259-267.

入库方式： OAI收割

来源：计算技术研究所

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