Personalized global citation recommendation with diversification awareness
Author:
Funder
The National Social Science Fund of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11192-024-05057-5.pdf
Reference55 articles.
1. Ali, Z., Kefalas, P., Muhammad, K., Ali, B., & Imran, M. (2020). Deep learning in citation recommendation models survey. Expert Systems with Applications, 162, 113790. https://doi.org/10.1016/j.eswa.2020.113790
2. Ali, Z., Qi, G. L., Muhammad, K., Bhattacharyya, S., Ullah, I., & Abro, W. (2021a). Citation recommendation employing heterogeneous bibliographic network embedding. In Neural Computing and Applications, 34, 10229–10242. https://doi.org/10.1007/s00521-021-06135-y
3. Ali, Z., Qi, G. L., Muhammad, K., Kefalas, P., & Khusro, S. (2021b). Global citation recommendation employing generative adversarial network. Expert Systems with Applications, 180, 114888. https://doi.org/10.1016/j.eswa.2021.114888
4. Ali, Z., Ullah, I., Khan, A., Jan, A. U., & Muhammad, K. (2021c). An overview and evaluation of citation recommendation models. Scientometrics, 126, 4083–4119. https://doi.org/10.1007/s11192-021-03909-y
5. Anand, A., Chakraborty, T., & Das, A. (2017). FairScholar: Balancing relevance and diversity for scientific paper recommendation. In European Conference on Information Retrieval, 10193, 753–757. Lecture Notes in Computer Science. Springer: Cham. https://doi.org/10.1007/978-3-319-56608-5_76
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