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An Improved LambdaMART Algorithm Based on the Matthew Effect

机译：基于马修效应的改进LambdaMART算法

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摘要

Matthew effect is a desirable phenomenon for a ranking model in search engines and recommendation systems. However, most of algorithms of learning to rank (LTR) do not pay attention to Matthew effect. LambdaMART is a well-known LTR algorithm that can be further optimized based on Matthew effect. Inspired by Matthew effect, we distinguish queries with different effectiveness and then assign a higher weight to a query with higher effectiveness. We improve the gradient in the LambdaMART algorithm to optimize the queries with high effectiveness, that is, to highlight the Matthew effect of the produced ranking models. In addition, we propose strategies of evaluating a ranking model and dynamically decreasing the learning rate to both strengthen the Matthew effect of ranking models and improve the effectiveness of ranking models. We use Gini coefficient, mean-variance, quantity statistics, and winning number to measure the performances of the ranking models. Experimental results on multiple benchmark datasets show that the ranking models produced by our improved LambdaMART algorithm can exhibit a stronger Matthew effect and achieve higher effectiveness compared to the original one and other state-of-the-art LTR algorithms.
机译：对于搜索引擎和推荐系统中的排名模型，马修效应是一种理想的现象。但是，大多数学习排名算法（LTR）都不注意马修效应。 LambdaMART是一种著名的LTR算法，可以根据马修效应对其进行进一步优化。受马修效应的启发，我们区分效果不同的查询，然后为效果更高的查询分配更高的权重。我们改进LambdaMART算法中的梯度，以高效地优化查询，即突出显示生成的排名模型的马修效应。此外，我们提出了评估排名模型并动态降低学习率的策略，以增强排名模型的马修效应和提高排名模型的有效性。我们使用基尼系数，均方差，数量统计和获胜次数来衡量排名模型的表现。在多个基准数据集上的实验结果表明，与原始的LTR算法和其他最新的LTR算法相比，我们改进的LambdaMART算法生成的排名模型可以表现出更强的Matthew效应并获得更高的有效性。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第14期|3082970.1-3082970.11|共11页
  • 作者

    Li Jinzhong; Liu Guanjun;

  • 作者单位

    Jinggangshan Univ, Dept Comp Sci & Technol, Coll Elect & Informat Engn, Jian 343009, Jiangxi, Peoples R China|Univ Elect Sci & Technol China, Network & Data Secur Key Lab Sichuan Prov, Chengdu 610054, Sichuan, Peoples R China;

    Tongji Univ, Dept Comp Sci & Technol, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China;

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