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 |
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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 | |
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 |
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Number | 3 |
Country/Territory | China |
City | Jinan |
Period | 08/07/2022 → 10/07/2022 |
Series | Communications in Computer and Information Science |
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Volume | 1637 CCIS |
ISSN | 1865-0929 |
Keywords
- Collaborators recommendation
- Meta-path features
- Network representation learning
- Scholars representation