A Collaborators Recommendation Method Based on Multi-feature Fusion

Qi Yuan; Lujiao Shao; Xinyu Zhang; Xinrui Yu; Huiyue Sun; Jianghong Ma; Weizhi Meng; Xiao Zhi Gao; Haijun Zhang

doi:10.1007/978-981-19-6142-7_19

A Collaborators Recommendation Method Based on Multi-feature Fusion

Qi Yuan
, Lujiao Shao
, Xinyu Zhang
, Xinrui Yu
, Huiyue Sun
, Jianghong Ma
, Weizhi Meng
, Xiao Zhi Gao
, Haijun Zhang^*

^*Corresponding author for this work

University of Eastern Finland
Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

This research introduces a new collaborators recommendation model based on multi-feature fusion. Specifically, we use a tree structure to integrate scholar information and extract content features from a scholar tree by using a Tree2vector-CLE model. Then, from the heterogeneous academic network, we extract the meta-path feature between scholars, which quantifies the similarity and co-operation potential between scholars from a multidimensional perspective. By combining content and meta-path features, we reconstruct a co-authorship network. Finally, we use the network representation learning method to represent the nodes in the reconstructed co-authorship network where the top-k collaborators are recommended for the target scholar with the random walk strategy controlled by the meta-path feature weighting. Experimental results on a real dataset demonstrate that our proposed method is effective in the task of collaborators recommendation.

Original language	English
Title of host publication	Neural Computing for Advanced Applications
Editors	Haijun Zhang, Yuehui Chen, Xianghua Chu, Zhao Zhang, Tianyong Hao, Zhou Wu, Yimin Yang
Publisher	Springer
Publication date	2022
Pages	247-261
ISBN (Print)	9789811961410
DOIs	https://doi.org/10.1007/978-981-19-6142-7_19
Publication status	Published - 2022
Event	Third International Conference on Neural Computing for Advanced Applications - Jinan, China Duration: 8 Jul 2022 → 10 Jul 2022 Conference number: 3

Conference

Conference	Third International Conference on Neural Computing for Advanced Applications
Number	3
Country/Territory	China
City	Jinan
Period	08/07/2022 → 10/07/2022

Series	Communications in Computer and Information Science
Volume	1637 CCIS
ISSN	1865-0929

Keywords

Collaborators recommendation
Meta-path features
Network representation learning
Scholars representation

Access to Document

10.1007/978-981-19-6142-7_19

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Cite this

@inproceedings{5068c0b485b24e639f9fd5d5c9a99cf1,

title = "A Collaborators Recommendation Method Based on Multi-feature Fusion",

abstract = "This research introduces a new collaborators recommendation model based on multi-feature fusion. Specifically, we use a tree structure to integrate scholar information and extract content features from a scholar tree by using a Tree2vector-CLE model. Then, from the heterogeneous academic network, we extract the meta-path feature between scholars, which quantifies the similarity and co-operation potential between scholars from a multidimensional perspective. By combining content and meta-path features, we reconstruct a co-authorship network. Finally, we use the network representation learning method to represent the nodes in the reconstructed co-authorship network where the top-k collaborators are recommended for the target scholar with the random walk strategy controlled by the meta-path feature weighting. Experimental results on a real dataset demonstrate that our proposed method is effective in the task of collaborators recommendation.",

keywords = "Collaborators recommendation, Meta-path features, Network representation learning, Scholars representation",

author = "Qi Yuan and Lujiao Shao and Xinyu Zhang and Xinrui Yu and Huiyue Sun and Jianghong Ma and Weizhi Meng and Gao, \{Xiao Zhi\} and Haijun Zhang",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; Third International Conference on Neural Computing for Advanced Applications, NCAA 2022 ; Conference date: 08-07-2022 Through 10-07-2022",

year = "2022",

doi = "10.1007/978-981-19-6142-7\_19",

language = "English",

isbn = "9789811961410",

series = "Communications in Computer and Information Science",

publisher = "Springer",

pages = "247--261",

editor = "Haijun Zhang and Yuehui Chen and Xianghua Chu and Zhao Zhang and Tianyong Hao and Zhou Wu and Yimin Yang",

booktitle = "Neural Computing for Advanced Applications",

}

Yuan, Q, Shao, L, Zhang, X, Yu, X, Sun, H, Ma, J, Meng, W, Gao, XZ & Zhang, H 2022, A Collaborators Recommendation Method Based on Multi-feature Fusion. in H Zhang, Y Chen, X Chu, Z Zhang, T Hao, Z Wu & Y Yang (eds), Neural Computing for Advanced Applications. Springer, Communications in Computer and Information Science, vol. 1637 CCIS, pp. 247-261, Third International Conference on Neural Computing for Advanced Applications, Jinan, China, 08/07/2022. https://doi.org/10.1007/978-981-19-6142-7_19

A Collaborators Recommendation Method Based on Multi-feature Fusion. / Yuan, Qi; Shao, Lujiao; Zhang, Xinyu et al.
Neural Computing for Advanced Applications. ed. / Haijun Zhang; Yuehui Chen; Xianghua Chu; Zhao Zhang; Tianyong Hao; Zhou Wu; Yimin Yang. Springer, 2022. p. 247-261 (Communications in Computer and Information Science, Vol. 1637 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - A Collaborators Recommendation Method Based on Multi-feature Fusion

AU - Yuan, Qi

AU - Shao, Lujiao

AU - Zhang, Xinyu

AU - Yu, Xinrui

AU - Sun, Huiyue

AU - Ma, Jianghong

AU - Meng, Weizhi

AU - Gao, Xiao Zhi

AU - Zhang, Haijun

N1 - Conference code: 3

PY - 2022

Y1 - 2022

N2 - This research introduces a new collaborators recommendation model based on multi-feature fusion. Specifically, we use a tree structure to integrate scholar information and extract content features from a scholar tree by using a Tree2vector-CLE model. Then, from the heterogeneous academic network, we extract the meta-path feature between scholars, which quantifies the similarity and co-operation potential between scholars from a multidimensional perspective. By combining content and meta-path features, we reconstruct a co-authorship network. Finally, we use the network representation learning method to represent the nodes in the reconstructed co-authorship network where the top-k collaborators are recommended for the target scholar with the random walk strategy controlled by the meta-path feature weighting. Experimental results on a real dataset demonstrate that our proposed method is effective in the task of collaborators recommendation.

AB - This research introduces a new collaborators recommendation model based on multi-feature fusion. Specifically, we use a tree structure to integrate scholar information and extract content features from a scholar tree by using a Tree2vector-CLE model. Then, from the heterogeneous academic network, we extract the meta-path feature between scholars, which quantifies the similarity and co-operation potential between scholars from a multidimensional perspective. By combining content and meta-path features, we reconstruct a co-authorship network. Finally, we use the network representation learning method to represent the nodes in the reconstructed co-authorship network where the top-k collaborators are recommended for the target scholar with the random walk strategy controlled by the meta-path feature weighting. Experimental results on a real dataset demonstrate that our proposed method is effective in the task of collaborators recommendation.

KW - Collaborators recommendation

KW - Meta-path features

KW - Network representation learning

KW - Scholars representation

U2 - 10.1007/978-981-19-6142-7_19

DO - 10.1007/978-981-19-6142-7_19

M3 - Article in proceedings

AN - SCOPUS:85142767851

SN - 9789811961410

T3 - Communications in Computer and Information Science

SP - 247

EP - 261

BT - Neural Computing for Advanced Applications

A2 - Zhang, Haijun

A2 - Chen, Yuehui

A2 - Chu, Xianghua

A2 - Zhang, Zhao

A2 - Hao, Tianyong

A2 - Wu, Zhou

A2 - Yang, Yimin

PB - Springer

T2 - Third International Conference on Neural Computing for Advanced Applications

Y2 - 8 July 2022 through 10 July 2022

ER -