Citation Count Prediction for Newly Published Papers
Author:
Affiliation:
1. Graduate School of Informatics, Nagoya University
Publisher
Japanese Society for Artificial Intelligence
Link
https://www.jstage.jst.go.jp/article/tjsai/39/5/39_39-5_B-O11/_pdf
Reference27 articles.
1. [Abrishami 19] Abrishami, A. and Aliakbary, S.: Predicting Citation Counts Based on Deep Neural Network Learning Techniques, Journal of Informetrics, Vol. 13, No. 2, pp. 485–499 (2019)
2. [Bhat 15] Bhat, H. S., Huang, L.-H., Rodriguez, S., Dale, R., and Heit, E.: Citation Prediction Using Diverse Features, 2015 IEEE International Conference on Data Mining Workshop (ICDMW 2015), pp. 589–596 (2015)
3. [Castillo 07] Castillo, C., Donato, D., and Gionis, A.: Estimating Number of Citations Using Author Reputation, in Ziviani, N. and Baeza-Yates, R. eds., String Processing and Information Retrieval (SPIRE 2007), pp. 107–117 (2007)
4. [Chakraborty 14] Chakraborty, T., Kumar, S., Goyal, P., Ganguly, N., and Mukherjee, A.: Towards a Stratified Learning Approach to Predict Future Citation Counts, in IEEE/ACM Joint Conference on Digital Libraries, pp. 351–360 (2014)
5. [Cortes 95] Cortes, C. and Vapnik, V. N.: Support-Vector Networks, Machine Learning, Vol. 20, pp. 273–297 (1995)
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