基于似然损失函数的组样本排序学习方法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201703005

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Abstract Group sample used for training the ranking model provides a new idea to construct learning to rank methods. In this paper, the new loss function is constructed for group samples to train the learning to rank model. The preference-weighted loss function and the initial ranking list optimization are employed to construct a new group learning to rank method based on neural network. Experimental results show that the proposed approach is effective in improving ranking performance.

Key words： Group Sample Information Retrieval Learning to Rank

Received: 10 November 2016

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61602078,61572102,61402075,61277370), China Postdoctoral Science Foundation(No.2016T90224,2015M581337), Fundamental Research Funds for the Central Universities(No.DUT15RW401)

About author:: LIN Yuan, born in 1983, Ph.D., lectu-rer. His research interests include information retrieval, learning to rank and machine lear-ning.
XU Bo,born in 1988, Ph.D. candidate. His research interests include learning to rank.
SUN Xiaoling, born in 1985, Ph.D., lecturer. Her research interests include data mining.
LIN Hongfei(Corresponding author), born in 1962, Ph.D., professor. His research interests include search engine, text mining and natural language understanding.
(XU Kan, born in 1981, Ph.D. candidate, engineer. His research interests include information retrieval.

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	LIN Yuan
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	LIN Hongfei
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Cite this article:

LIN Yuan,XU Bo,SUN Xiaoling等. Group Sample Learning to Rank Approach Based on Likelihood Loss Function[J]. , 2017, 30(3): 235-241.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201703005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I3/235

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