TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Open Access Research Article

Optimizing learning to rank models with neural network-based feature selection techniques

* ,

* Corresponding author · click or hover a name for details

pp. 541–551Vol. 46Issue 2March 2025DOI: 10.47974/JIOS-1958XML
Received:
13 Nov 2024
Published Online:
17 Mar 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1958
Pages:
541–551

Abstract

Learning-to-rank (LTR) is a very important task in information retrieval. However, in today’s era of exponentially growing data volumes available over the internet, acquiring high-quality data for feature selection poses a significant challenge in LTR. The emerging method of employing neural network-based feature selection in learning to rank utilizes machine learning methodologies to identify and choose relevant features, to enhanced efficiency and improve the ranking. This strategy overcome the constraints of conventional LTR methods and enhances the ranking performance across diverse applications. This research presents a new iteration of a sequential neural network model through the utilization of existing models, along with an analysis of its attributes. A distinctive approach to feature selection utilizing neural networks is outlined in this research for learning to rank, employing LambdaMART, a gradient boosting algorithm, to enhance the effectiveness and efficiency of ranking protocols on extensive datasets. The proposed method performance is better than the few existing state-of-arts algorithms. The model performance found as 0.711 NDCG, 0.61 P@K,0.611 on MSLR Web 30k and proposed model useful in many real time ranking applications.

Keywords

Subject Classifications

68T0592B20

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).

Views: 245Downloads: 120Citations: 0