Recommending Research Collaborators Based on Scholar Profiling

doi:10.11925/infotech.2096-3467.2021.1457

Data Analysis and Knowledge Discovery

2022, Vol. 6

Issue (10): 20-34 DOI: 10.11925/infotech.2096-3467.2021.1457

Current Issue | Archive | Adv Search

Recommending Research Collaborators Based on Scholar Profiling

Dong Wenhui,Xiong Huixiang,Du Jin,Wang Niuniu(

)

School of Information Management, Central China Normal University, Wuhan 430079, China

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Abstract

[Objective] This paper helps scholars quickly find suitable scientific research partners, and then promote research output and enhance academic exchanges. [Methods] First, we explored the four dimensional characteristics of scholars’ natural attributes, interest attributes, ability attributes and social attributes with the LDA topic model, PageRank algorithm and social network analysis. Then, we constructed scholars’ profiles, and recommended collaborators based on their preferences. [Results] We examined the proposed method with 14 007 articles, 13 292 citations and 11 869 authors in the field of Library and Information Science from the CNKI and CSSCI databases. A total of 20 potential collaborators with similar and complementary research interests were recommended to the target scholars. [Limitations] More research is needed to address the cold start issue, as well as the contribution of authors in different signing orders of the papers. The data of the empirical study also needs to be expanded. [Conclusions] The proposed model can effectively recommend potential research collaborators for the target scholars, which has good application value.

Key words： Scholar Profiling Recommendation of Research Collaborators Latent Dirichlet Allocation PageRank Social Network Analysis

Received: 27 December 2021 Published: 16 November 2022

ZTFLH:

G203

Fund:National Social Science Fund of China(19ZDA345)

Corresponding Authors: Wang Niuniu,ORCID：0000-0003-1513-3503 E-mail: w0815@mails.ccnu.edu.cn

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Cite this article:

Dong Wenhui, Xiong Huixiang, Du Jin, Wang Niuniu. Recommending Research Collaborators Based on Scholar Profiling. Data Analysis and Knowledge Discovery, 2022, 6(10): 20-34.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1457 OR https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I10/20

Scholar Recommendation Framework Based on Scholar Portrait

Academic Age Classification of Scholars

Educational Background or Professional Title Classification of Scholars

Organization Classification

Scholar Portrait Index System

标度	含义（比较因素i与j）
1	因素i与j相比，同样重要
3	因素i与j相比，稍微重要
5	因素i与j相比，较强重要
7	因素i与j相比，强烈重要
9	因素i与j相比，极端重要
2、4、6、8	上述两个相邻判断因素的中间值
倒数	因素j与i相比得到的判断矩阵为Z_ji= $1 Z i j$

Scaling Method of Judgment Matrix

Summary of Literature Information

Information of Scholars

Academic Age Similarity Score

Reciprocal of Geographical Distance Matrix of Provinces

Chart of Subject Number Consistency

Similarity Results of Scholars’ Interests

Complementarity Results of Scholars’ Interests

Calculation Results of

h T

Authority Scores of Academic Institutions

Calculation Results of PageRank Value Cited by Scholars

Scholar Cooperation Network

Institutional Cooperation Network

Calculation Results of Scholar Centrality

Calculation Results of Institution Centrality

Weight Results of Four Dimensions of Experts

Recommended Results of Scientific Research Collaborators with Similar Interests

Recommended Results of Scientific Research Collaborators with Complementary Interests

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