Optimizing learning to rank models with neural network-based feature selection techniques
*Sushilkumar ChavhanCorresponding authorschavhansushil@gmail.comDepartment of Information TechnologyYeshwantrao Chavan College of EngineeringNagpur, Maharashtra, 441110, IndiaView full profile → , R. C. Dharmikraj_dharmik@yahoo.comDepartment of Information TechnologyYeshwantrao Chavan College of EngineeringNagpur, Maharashtra, 441110, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 13 Nov 2024
- Published Online:
- 17 Mar 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1958
- Pages:
- 541–551
Abstract
Keywords
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References
[1] S. Jabri, A. Dahbi, T. Gadi, and A. Bassir, “Ranking of Text Documents using TF-IDF Weighting and Association Rules mining,” IEEE (2018).
[2] X. Wu, W. Zeng, F. Lin, and X. Zhou, “NeuRank: learning to rank with neural networks for drug-target interaction prediction,” BMC Bioinformatics (2021).
[3] R. K. Pasumarthi, S. Bruch, X. Wang, C. Li, M. Bendersky, M. Najork, J. Pfeifer, N. Golbandi, R. Anil, and S. Wolf, “TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank,” Proc. KDD ‘19: Applied Data Science Track Paper, Anchorage, AK, USA, pp. 1–9 (2019).
[4] S. Han, X. Wang, M. Bendersky, and M. Najork, “Learning-To-Rank With BERT In TF-Ranking,” (2020).
[5] C. Prakash and A. Sarkar, “Ranking with Deep Neural Networks,” 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT), Kolkata, India, pp. 1–4 (2018).
[6] M. Köppel, A. Segner, M. Wagener, L. Pensel, A. Karwath, and S. Kramer, “Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance,” Lecture Notes in Computer Science (LNAI), vol. 11908, pp. 1–12 (2020).
[7] A. Rahangdale and S. Raut, “Deep Neural Network Regularization for Feature Selection in Learning-to-Rank,” IEEE Access, vol. 7, pp. 53988–54006 (2019).
[8] C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender, “Learning to rank using gradient descent,” in Proc. 22nd International Conference on Machine Learning (ICML), pp. 89–96 (2005).
[9] S. Sharma, L. Raja, V. Bhatnagar, D. Sharma, S. N. Bhagirath, and R. C. Poonia, “Hybrid HOG-SVM encrypted face detection and recognition model,” J. Discrete Math. Sci. Cryptography, vol. 25, no. 1, pp. 205–218 (2022).
[10] Z. Cao, T. Qin, T. Y. Liu, M. F. Tsai, and H. Li, “Learning to rank: From pairwise approach to listwise approach,” (2007).
[11] L. Yu, Y. Wang, X. Sun, K. Bi, and J. Guo, “Feature-Enhanced Network with Hybrid Debiasing Strategies for Unbiased Learning to Rank,” in Proc. 16th ACM International Conference on Web Search and Data Mining (WSDM), pp. 1–12 (2023).
[12] A. Purpura, K. Buchner, G. Silvello, and G. A. Susto, “Neural Feature Selection for Learning to Rank,” in Advances in Information Retrieval, D. Hiemstra et al., Eds., Springer, pp. 1–10 (2021).
[13] U. Ergashev, E. Dragut, and W. Meng, “Learning To Rank Resources with GNN,” in Proc. ACM Web Conference 2023 (WWW ‘23), pp. 3247–3256 (2023).
[14] C. Prakash and A. Sarkar, “Ranking with Deep Neural Networks,” 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT), Kolkata, India, pp. 1–4 (2018).
[15] D. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” International Conference on Learning Representations (ICLR) (2014).
[16] T. Qin and T. Y. Liu, “Introducing LETOR 4.0 datasets,” (2013).
[17] N. P. Sejdiu, B. Ristevski, and I. Jolevski, “Performance Comparison of Machine Learning Algorithms in Movie Recommender Systems,” 2022 57th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), Ohrid, North Macedonia, pp. 1–4 (2022).
[18] C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender, “Learning to rank using gradient descent,” in Proc. 22nd International Conference on Machine Learning (ICML), pp. 89–96 (2005).




