TY - GEN
T1 - Recommendation for Cross-Disciplinary Collaboration Based on Potential Research Field Discovery
AU - Liang, Wei
AU - Zhou, Xiaokang
AU - Huang, Suzhen
AU - Hu, Chunhua
AU - Jin, Qun
N1 - Funding Information:
ACKNOWLEDGMENT The work has been partially supported by 2015 and 2016 Waseda University Grants for Special Research Projects No. 2015B-381 and 2016B-233, the National Science Foundation of China under Grant No. 61273232, 61472136 and the Program for New Century Excellent Talents in University under NCET-13-0785, the Hunan Provincial Education Department Foundation for Excellent Youth Scholars under Grant No. 17B146. The authors are grateful to the suggestions from Prof. Weijin Jiang for this work.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/6
Y1 - 2017/9/6
N2 - In recent years, cross-disciplinary scientific collaboration has been proved to be promising for both research practice and innovation. Lots of efforts have been spent in collaboration recommendation. However, the cross-disciplinary information is hidden in tons of publications, and the relationships between different fields are complicated, which make it challengeable recommending cross-disciplinary collaboration for a specific researcher. In this paper, a novel cross-disciplinary collaboration recommendation method (CDCR) that unearths the common cross-disciplinary collaboration patterns and historical scientific field preferences of authors is proposed to recommend potential cross-disciplinary research collaboration. In CDCR, a research field discovery algorithm is designed to classify scientific topics obtained from the publications into the correct field automatically. Then, the collaborative patterns are studied through analyzing the composition fields and the corresponding percentage of all publications. Furthermore, we investigate the common correlation of different research fields. Based on the common correlation and the researcher's specific pattern, the most valuable fields will be listed by CDCR. The effectiveness of our approach is evaluated based on a real academic dataset.
AB - In recent years, cross-disciplinary scientific collaboration has been proved to be promising for both research practice and innovation. Lots of efforts have been spent in collaboration recommendation. However, the cross-disciplinary information is hidden in tons of publications, and the relationships between different fields are complicated, which make it challengeable recommending cross-disciplinary collaboration for a specific researcher. In this paper, a novel cross-disciplinary collaboration recommendation method (CDCR) that unearths the common cross-disciplinary collaboration patterns and historical scientific field preferences of authors is proposed to recommend potential cross-disciplinary research collaboration. In CDCR, a research field discovery algorithm is designed to classify scientific topics obtained from the publications into the correct field automatically. Then, the collaborative patterns are studied through analyzing the composition fields and the corresponding percentage of all publications. Furthermore, we investigate the common correlation of different research fields. Based on the common correlation and the researcher's specific pattern, the most valuable fields will be listed by CDCR. The effectiveness of our approach is evaluated based on a real academic dataset.
KW - Cross-disciplinary
KW - Data mining
KW - Research field discovery
KW - Scientific collaboration
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U2 - 10.1109/CBD.2017.67
DO - 10.1109/CBD.2017.67
M3 - Conference contribution
AN - SCOPUS:85031713986
T3 - Proceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017
SP - 349
EP - 354
BT - Proceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Advanced Cloud and Big Data, CBD 2017
Y2 - 13 August 2017 through 16 August 2017
ER -