Hybrid Learning-to-Rank Approach for Complex Information Retrieval Systems

Abstract: Biomedical question answering presents significant challenges due to the complexity of biomedical language and the need for precise information retrieval. This study aims to improve the performance of a biomedical information retrieval system through a hybrid learning-to-rank framework. Specifically, we combine lexical (BM25) and semantic (BioBERT) representations to form hybrid inputs for RankFormer, a transformer-based ranking model. This hybrid representation captures both surface-level term matching and deep contextual understanding. Experiments conducted on the BioASQ dataset show that our approach achieves better ranking performance compared to the standalone lexical or neural baselines, reaching a MAP@10 of 0.9614 and an nDCG@10 of 0.9320. These results highlight the effectiveness of hybrid input representations in enhancing biomedical answer ranking.

Fatma Zohra Bessai-Mechmache, Yasmine Hanifi and Damia Lyna Ait Idir. “Hybrid Learning-to-Rank Approach for Complex Information Retrieval Systems”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.4 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170443

@article{Bessai-Mechmache2026,
title = {Hybrid Learning-to-Rank Approach for Complex Information Retrieval Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170443},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170443},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {4},
author = {Fatma Zohra Bessai-Mechmache and Yasmine Hanifi and Damia Lyna Ait Idir}
}