Cloud-Native Scalable Learning-to-Rank Architecture for Real-Time Search Optimization
DOI:
https://doi.org/10.22399/ijcesen.5118Keywords:
Learning-to-Rank, Cloud-Native Architecture, Real-Time Search Optimization, Scalable Microservices, Machine Learning Ranking, Distributed Search SystemsAbstract
Today, search engines must be capable of delivering extremely relevant results while handling a large number of queries simultaneously in real time. A cloud-native, scalable Learning to Rank (LTR) system can leverage distributed computing, microservices, and on-demand cloud resources for the efficient search optimization task. This approach will incorporate machine learning based ranking models into the search pipeline, thereby enabling the system to re-evaluate and re-order the results in line with user intent, contextual signals, and historical interaction data in a dynamic manner. The proposed architecture utilizes containerized services, stream processing, and scalable storage for real, time feature extraction, model inference, and continuous model updates. By making use of orchestration platforms and serverless components, the system can automatically scale without any human intervention even when workload fluctuations are extreme. It will also maintain low latency and high availability. Data pipelines collect clickstream and behavioral data which are then used to train and periodically retrain the LTR models so that the ranking strategies always reflect the changing preferences of the users. Besides, the design includes facilities for A/B testing, monitoring, and model versioning which give a way to controlled experimentation and performance optimization. Using caching layers and distributed indexing mechanisms results in improved response times and reduced computational overhead.
References
[1] Liu, T. Y. (2009). Learning to rank for information retrieval. Foundations and Trends® in Information Retrieval, 3(3), 225-331.
[2] Li, H. (2011). A short introduction to learning to rank. IEICE TRANSACTIONS on Information and Systems, 94(10), 1854-1862.
[3] Li, H. (2014). Learning to rank for information retrieval and natural language processing. Morgan & Claypool Publishers.
[4] Dang, V., Bendersky, M., & Croft, W. B. (2013, March). Two-stage learning to rank for information retrieval. In European Conference on Information Retrieval (pp. 423-434). Berlin, Heidelberg: Springer Berlin Heidelberg.
[5] Bruch, S., Gai, S., & Ingber, A. (2023). An analysis of fusion functions for hybrid retrieval. ACM Transactions on Information Systems, 42(1), 1-35.
[6] Burges, C. J. (2010). From ranknet to lambdarank to lambdamart: An overview. Learning, 11(23-581), 81.
[7] Cao, Z., Qin, T., Liu, T. Y., Tsai, M. F., & Li, H. (2007, June). Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th international conference on Machine learning (pp. 129-136).
[8] Joachims, T. (2002, July). Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 133-142).
[9] Wang, B., & Klabjan, D. (2017). An attention-based deep net for learning to rank. arXiv preprint arXiv:1702.06106.
[10] Gomes, G. D. C. M., de Oliveira, V. C., de Almeida, J. M., & Gonçalves, M. A. (2013). Is learning to rank worth it? a statistical analysis of learning to rank methods. arXiv preprint arXiv:1303.2277.
[11] Moreira, C., Calado, P., & Martins, B. (2011, October). Learning to rank for expert search in digital libraries of academic publications. In Portuguese conference on artificial intelligence (pp. 431-445). Berlin, Heidelberg: Springer Berlin Heidelberg.
[12] Dong, X., Chen, X., Guan, Y., Li, S., & Xu, Z. (2009, March). An overview of learning to rank for information retrieval. In 2009 WRI World Congress on Computer Science and Information Engineering (Vol. 3, pp. 600-606). IEEE.
[13] Zhao, T., Cao, Q., & Sun, Q. (2017, December). An improved approach to traceability recovery based on word embeddings. In 2017 24th Asia-Pacific Software Engineering Conference (APSEC) (pp. 81-89). IEEE.
[14] Tian, Q., Cao, Q., & Sun, Q. (2018, July). Adapting word embeddings to traceability recovery. In 2018 International conference on information systems and computer aided Education (ICISCAE) (pp. 255-261). IEEE.
[15] Preethi, P., Saravanan, T., Mohanraj, R., & Gayathri, P. G. (2024). A real-time environmental air pollution predictor model using a dense deep learning approach in IoT infrastruc- ture. GLOBAL NEST JOURNAL, 26(3).
[16] Pamulaparthyvenkata, S., Sharma, J., Dattangire, R., Vishwanath, M., Mulukuntla, S., Preethi, P., & Indhumathi, N. (2024, June). Deep Learning and EHR-Driven Image Processing Frame- work for Lung Infection Detection in Healthcare Applications. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
[17] Raj, R. R. M., Saravanan, T., Preethi, P., & Ezhilarasi, I. (2022). Comparative evaluation of efficacy of therapeutic ultrasound and phonophoresis in myofascial pain dysfunction syn- drome. Journal of Indian Academy of Oral Medicine and Radiology, 34(3), 242-245.
[18] Chohan, M. A., Farooqi, M. A., Raza, A., Rasheed, M. N., & Shahzad, K. (2024). ARTIFICIAL INTELLIGENCE AND INTELLECTUAL PROPERTY RIGHTS: FROM CONTENT CREATION TO OWNERSHIP.
[19] Raza, A., & Bashir, N. (2023). Artificial intelligence as a creator and inventor: legal challenges and protections in copyright, patent, and trademark law. Artificial Intelligence as a Creator and Inventor: Legal Challenges and Protections in Copyright, Patent, and Trademark Law (December 31, 2023).
[20] Singh, B. (2023). Software-Defined Data Centers: Innovations in Network Architecture for High Availability. Available at SSRN 5331661.
[21] Ergashev, U., Dragut, E., & Meng, W. (2023, April). Learning to rank resources with GNN. In Proceedings of the ACM Web Conference 2023 (pp. 3247-3256).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
