Journal of Guangxi Normal University(Natural Science Edition) ›› 2019, Vol. 37 ›› Issue (1): 62-70.doi: 10.16088/j.issn.1001-6600.2019.01.007

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Group Ranking Methods with Loss Function Incorporation

LIN Yuan1, LIU Haifeng2, LIN Hongfei2, XU Kan2*   

  1. 1.Faculty of Humanities and Social Sciences, Dalian University of Technology, Dalian Liaoning 116024,China;
    2.Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian Liaoning 116024,China
  • Received:2018-09-27 Online:2019-01-20 Published:2019-01-08

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Abstract: Learning to rank has been attracted much attention in the domain of information retrieval and machine learning. A series of learning to rank algorithms have been proposed based on three types of methods, namely, pointwise, pairwise and listwise. Especially, ranking performance can be improved effectively by one of the listwise methods named group ranking. This paper explores how to combine the loss functions from these methods to improve group ranking performance. The basic idea is to incorporate the different loss functions and enrich the objective loss function based on neural networks. Firstly, a group learning to rank method based on Jeffrey’s divergence is presented. Secondly, a framework for loss function incorporation based on group ranking method and the other loss function is presented. The performance of the proposed method is compared on LETOR3.0 dataset, which demonstrates that with a good weighting scheme. Finally, experimental results show that the proposed method significantly outperforms the baselines which use single loss function, and it is comparable to the state-of-the-art algorithms in most cases.

Key words: learning to rank, information retrieval, neural network, loss function, Jeffrey’s divergence

CLC Number: 

  • TP391
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