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题名:
listopt: learning to optimize for xml ranking
作者: Gao Ning ; Deng Zhi-Hong ; Yu Hang ; Jiang Jia-Jian
会议文集: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
会议名称: 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011
会议日期: 24-May-20
出版日期: 2011
会议地点: Shenzhen, China
关键词: Adaptive boosting ; Data mining ; Information retrieval ; Neural networks ; XML
出版地: Germany
收录类别: ei
ISSN: 3029743
ISBN: 9783642208461
部门归属: (1) Key Laboratory of Machine Perception (Ministry of Education), School of Electronic Engineering and Computer Science, Peking University, China; (2) State Key Lab. of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
英文摘要: Many machine learning classification technologies such as boosting, support vector machine or neural networks have been applied to the ranking problem in information retrieval. However, since the purpose of these learning-to-rank methods is to directly acquire the sorted results based on the features of documents, they are unable to combine and utilize the existing ranking methods proven to be effective such as BM25 and PageRank. To solve this defect, we conducted a study on learning-to-optimize, which is to construct a learning model or method for optimizing the free parameters in ranking functions. This paper proposes a listwise learning-to-optimize process ListOPT and introduces three alternative differentiable query-level loss functions. The experimental results on the XML dataset of Wikipedia English show that these approaches can be successfully applied to tuning the parameters used in an existing highly cited ranking function BM25. Furthermore, we found that the formulas with optimized parameters indeed improve the effectiveness compared with the original ones. © 2011 Springer-Verlag.
语种: 英语
内容类型: 会议论文
URI标识: http://ir.iscas.ac.cn/handle/311060/14269
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Gao Ning,Deng Zhi-Hong,Yu Hang,et al. listopt: learning to optimize for xml ranking[C]. 见:15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011. Shenzhen, China. 24-May-20.
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