Robust Learning to Rank Based on Portfolio Theory and AMOSA Algorithm
Robust Learning to Rank Based on Portfolio Theory and AMOSA Algorithm
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基于投资组合理论和AMOSA算法的鲁棒学习排序
DOI：
10.1109/tsmc.2016.2584786
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发表时间：
2017-06
影响因子：
8.7
通讯作者：
Jiang Changjun
中科院分区：
文献类型：
--
作者：
Li Jinzhong;Liu Guanjun;Yan Chungang;Jiang Changjun
在学习排序（LTR）算法所产生的排序模型中，有效性是最重要的考虑因素。大多数相关排序模型只专注于提高平均有效性，却忽略了稳健性。当一个排序模型……
Effectiveness is the most important factor considered in the ranking models yielded by algorithms of learning to rank (LTR). Most of the related ranking models only focus on improving the average effectiveness but ignore robustness. When a ranking model ignores robustness, the effectiveness for many queries is possibly very poor although the average effectiveness for all queries is relatively high. Therefore, Wang <italic>et al.</italic> first consider robustness in their ranking models. However, the robustness formula defined by Wang <italic>et al.</italic> cannot characterize those queries whose effectiveness are hurt seriously in comparison with the baseline model. In order to overcome this shortcoming, we propose a novel formula of characterizing robustness based on portfolio theory, and construct a multiobjective optimization model of the robust LTR in which the formula is used. Based on this model, we propose an approach of risk-sensitive and robust LTR, named as <inline-formula> <tex-math notation="LaTeX">$\text{R}^{ 2}$ </tex-math></inline-formula>Rank, which is based on the framework of archived multiobjective simulated annealing algorithm and the idea of preference ranking organization method for enrichment evaluation. The experimental results show that the ranking models produced by our proposed <inline-formula> <tex-math notation="LaTeX">$\text{R}^{ 2}$ </tex-math></inline-formula>Rank approach are better in both effectiveness and robustness than those produced by three state-of-the-art LTR approaches.
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DOI：
10.1145/1571941.1571963
发表时间：
2009-07
期刊：
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
影响因子：
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作者：
Jun Wang;Jianhan Zhu
通讯作者：
Jun Wang;Jianhan Zhu
DOI：
10.1109/tsmca.2012.2217322
发表时间：
2013-02
期刊：
IEEE Transactions on Systems, Man, and Cybernetics: Systems
影响因子：
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作者：
F. Almeida;D. Giménez;J. López-Espín;Melquíades Pérez Pérez-Melquíades-Pérez-Pérez-2311409
通讯作者：
F. Almeida;D. Giménez;J. López-Espín;Melquíades Pérez Pérez-Melquíades-Pérez-Pérez-2311409
DOI：
10.1145/1390334.1390355
发表时间：
2008-07
期刊：
--
影响因子：
--
作者：
Jun Xu;Tie-Yan Liu;Min Lu;Hang Li;Wei-Ying Ma
通讯作者：
Jun Xu;Tie-Yan Liu;Min Lu;Hang Li;Wei-Ying Ma
DOI：
10.7551/mitpress/7503.003.0029
发表时间：
2006-12
期刊：
--
影响因子：
--
作者：
C. Burges;R. Ragno;Quoc V. Le
通讯作者：
C. Burges;R. Ragno;Quoc V. Le
DOI：
10.1007/s10791-009-9112-1
发表时间：
2010-06
期刊：
Information Retrieval
影响因子：
--
作者：
通讯作者：
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