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International Conference on Computational Social Networks

CSoNet 2018: Computational Data and Social Networks pp 450–459Cite as

A Rich Ranking Model Based on the Matthew Effect Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11280))

Abstract

Most existing approaches of learning to rank treat the effectiveness of each query equally which results in a relatively lower ratio of queries with high effectiveness (i.e. rich queries) in the produced ranking model. Such ranking models need to be further optimized to increase the number of rich queries. In this paper, queries with different effectiveness are distinguished, and the queries with higher effectiveness are given higher weights. We modify the gradient in the LambdaMART algorithm based on a new perspective of Matthew effect to highlight the optimization of the rich queries and to produce the rich ranking model, and we present a consistency theorem for the modified optimization objective. Based on the effectiveness evaluation criteria for information retrieval, we introduce the Gini coefficient, mean-variance and quantity statistics to measure the performances of the ranking models. Experimental results show that the ranking models produced by the gradient-modified LambdaMART algorithm based on Matthew effect exhibit a stronger Matthew effect compared to the original LambdaMART algorithm.

Supported by the National Natural Science Foundation of China under Grant Nos. 61762052 and 61572360, the Natural Science Foundation of JiangXi Province of China under Grant No. 20171BAB202010, the Opening Foundation of Network and Data Security Key Laboratory of Sichuan Province under Grant No. NDSMS201602, the Science and Technology Project of the Education Department of Jiangxi Province of China under Grant No. GJJ160746, and the Doctoral Scientific Research Startup Foundation of Jinggangshan University under Grant No. JZB1804.

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Notes

  1. 1.

    http://sourceforge.net/p/lemur/code/HEAD/tree/RankLib/trunk/.

  2. 2.

    http://research.microsoft.com/en-us/projects/mslr/download.aspx.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, College of Electronic and Information Engineering, Jinggangshan University, Ji’an, 343009, China

    Jinzhong Li

  2. Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Jinzhong Li

  3. Department of Computer Science and Technology, College of Electronic and Information Engineering, Tongji University, Shanghai, 201804, China

    Guanjun Liu

  4. Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, 201804, China

    Guanjun Liu

Authors
  1. Jinzhong Li
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  2. Guanjun Liu
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Corresponding author

Correspondence to Jinzhong Li .

Editor information

Editors and Affiliations

  1. Texas Southern University, Houston, TX, USA

    Xuemin Chen

  2. Ira A. Fulton School of Engineering, Tempe, AZ, USA

    Arunabha Sen

  3. Texas Southern University, Houston, TX, USA

    Wei Wayne Li

  4. University of Florida, Gainesville, AL, USA

    My T. Thai

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Li, J., Liu, G. (2018). A Rich Ranking Model Based on the Matthew Effect Optimization. In: Chen, X., Sen, A., Li, W., Thai, M. (eds) Computational Data and Social Networks. CSoNet 2018. Lecture Notes in Computer Science(), vol 11280. Springer, Cham. https://doi.org/10.1007/978-3-030-04648-4_38

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  • DOI: https://doi.org/10.1007/978-3-030-04648-4_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04647-7

  • Online ISBN: 978-3-030-04648-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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