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Cluster Computing

Erschienen in:

03.01.2018

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

verfasst von: Wanli Feng, Quanyin Zhu, Jun Zhuang, Shimin Yu

Erschienen in: Cluster Computing | Sonderheft 3/2019

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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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Titel: An expert recommendation algorithm based on Pearson correlation coefficient and FP-growth
verfasst von: Wanli Feng
Quanyin Zhu
Jun Zhuang
Shimin Yu
Publikationsdatum: 03.01.2018
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe Sonderheft 3/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-1576-y

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