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

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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.

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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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Authors and Affiliations

  1. School of Computer and Software Engineer, Huaiyin Institute of Technology, Huai’an, 223003, JiangSu, China

    Wanli Feng, Quanyin Zhu, Jun Zhuang & Shimin Yu

  2. Huai’an FunSoft Co., Huai’an, 223005, JiangSu, China

    Wanli Feng & Jun Zhuang

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  1. Wanli Feng
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  2. Quanyin Zhu
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  3. Jun Zhuang
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  4. Shimin Yu
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Correspondence to Wanli Feng.

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Feng, W., Zhu, Q., Zhuang, J. et al. An expert recommendation algorithm based on Pearson correlation coefficient and FP-growth. Cluster Comput 22 (Suppl 3), 7401–7412 (2019). https://doi.org/10.1007/s10586-017-1576-y

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  • DOI: https://doi.org/10.1007/s10586-017-1576-y

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