Please wait a minute...
Data Analysis and Knowledge Discovery
Current Issue | Archive | Adv Search |
Research on Collaborator Recommendation based on Scholar Profiling
Dong Wenhui,XIONG Hui-xiang,Du Jin,Wang Niu niu
(School of Information Management, Central China Normal University, Wuhan 430079, China)
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] In order to help scholars quickly find suitable scientific research partners, promote scientific research output and enhance academic exchanges.

[Methods] Using LDA topic model, PageRank algorithm and social network analysis, this paper comprehensively and deeply excavates the four dimensional characteristics of scholars' natural attributes, interest attributes, ability attributes and social attributes to construct scholars' portraits,and recommend scientific research collaborators based on scholars' preferences.

[Results] Finally, 14007 documents, 13292 citation data and 11869 authors in the field of Library and information were obtained from CNKI and CSSCI to verify the model proposed in this paper. Finally, 20 potential scientific research collaborators with similar and complementary research interests were recommended to the target scholars..

[Limitations] This paper fails to solve the cold start problem well, and ignores the contribution of authors in different signing orders to the paper in terms of scholars' ability representation, and the selection of data in the empirical link is limited.

[Conclusion] This model can effectively recommend potential scientific research collaborators with high authority, high relevance, and high matching characteristics such as scientific research productivity and social relations to target scholars, and has good application value.

Key words scholar profiling      recommendation of research collaborators      LDA      PageRank      social network analysis      
Published: 28 March 2022
ZTFLH:  G203  

Cite this article:

Dong Wenhui, XIONG Hui-xiang, Du Jin, Wang Niu niu. Research on Collaborator Recommendation based on Scholar Profiling . Data Analysis and Knowledge Discovery, 0, (): 1-.

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/Y0/V/I/1

[1] Shi Xiang, Liu Ping. Review of Studies Identifying Research Interests[J]. 数据分析与知识发现, 2022, 6(4): 16-27.
[2] Yue Tieqi, Fu Youfei, Xu Jian. An Analysis Framework for Job Demands from Job Postings[J]. 数据分析与知识发现, 2022, 6(2/3): 151-166.
[3] Zhou Yunze, Min Chao. Identifying Emerging Technology with LDA Model and Shared Semantic Space——Case Study of Autonomous Vehicles[J]. 数据分析与知识发现, 2022, 6(2/3): 55-66.
[4] Dong Wenhui, Xiong Huixiang, Du Jin, Wang Niuniu. Recommending Research Collaborators Based on Scholar Profiling[J]. 数据分析与知识发现, 2022, 6(10): 20-34.
[5] Linna Xi,Yongxiang Dou. Examining Reposts of Micro-bloggers with Planned Behavior Theory[J]. 数据分析与知识发现, 2019, 3(2): 13-20.
[6] Jie Zhang,Junbo Zhao,Dongsheng Zhai,Ningning Sun. Patent Technology Analysis of Microalgae Biofuel Industrial Chain Based on Topic Model[J]. 数据分析与知识发现, 2019, 3(2): 52-64.
[7] Junwan Liu,Zhixin Long,Feifei Wang. Finding Collaboration Opportunities from Emerging Issues with LDA Topic Model and Link Prediction[J]. 数据分析与知识发现, 2019, 3(1): 104-117.
[8] Guijun Yang,Xue Xu,Fuqiang Zhao. Predicting User Ratings with XGBoost Algorithm[J]. 数据分析与知识发现, 2019, 3(1): 118-126.
[9] Wang Li,Zou Lixue,Liu Xiwen. Visualizing Document Correlation Based on LDA Model[J]. 数据分析与知识发现, 2018, 2(3): 98-106.
[10] Li He,Zhu Linlin,Yan Min,Liu Jincheng,Hong Chuang. Identifying Useful Information from Open Innovation Community[J]. 数据分析与知识发现, 2018, 2(12): 12-22.
[11] Qu Jiabin,Ou Shiyan. Analyzing Topic Evolution with Topic Filtering and Relevance[J]. 数据分析与知识发现, 2018, 2(1): 64-75.
[12] Chen Xiaowei,Shi Yutian. Identifying Key Nodes in Social Network with Improved PageRank Algorithm[J]. 数据分析与知识发现, 2017, 1(8): 68-75.
[13] Guan Peng,Wang Yuefen. Identifying Optimal Topic Numbers from Sci-Tech Information with LDA Model[J]. 现代图书情报技术, 2016, 32(9): 42-50.
[14] Qun Zhang, Hongjun Wang, Lunwen Wang. Classifying Short Texts with Word Embedding and LDA Model[J]. 数据分析与知识发现, 2016, 32(12): 27-35.
[15] Zhuo Keqiu, Yu Wei, Su Xinning. Parallel Implementing Bursty Events Detection Using MapReduce[J]. 现代图书情报技术, 2015, 31(2): 46-54.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn