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An expert recommendation algorithm based on Pearson correlation coefficient and FP-growth

  • Wanli Feng
  • Quanyin Zhu
  • Jun Zhuang
  • Shimin Yu
Article

Abstract

In order to recommend an efficient drawing inspecting expert combination, an expert combination is selected by an expert recommendation algorithm based on Pearson’s correlation coefficient and FP-growth. By introducing the Pearson correlation coefficient and the FP-growth association rule algorithm, the expert recommendation algorithm can accurately select the participating experts in the historical project similar to the scale of the project to be reviewed, and combine the experts to calculate and obtain the expert group with the highest fit, namely, the expert combination of project to be reviewed. This expert recommendation algorithm based on Pearson correlation coefficient and FP-growth can effectively recommend a kind of expert group with the highest efficiency of collaborative review, which solves the problem of how to recommend efficient expert combination accurately for drawing inspecting system.

Keywords

Pearson correlation coefficient FP-growth Combination expert recommendation 

Notes

Acknowledgements

This work was supported by Jiangsu Province the Fifth "333 Project", and Huai’an City Production and Research Projects under Grant nos. HAC2015008 and HAS201621.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Wanli Feng
    • 1
    • 2
  • Quanyin Zhu
    • 1
  • Jun Zhuang
    • 1
    • 2
  • Shimin Yu
    • 1
  1. 1.School of Computer and Software EngineerHuaiyin Institute of TechnologyHuai’anChina
  2. 2.Huai’an FunSoft Co.Huai’anChina

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