Using Neural Architecture Investigations and Automated Machine Learning to Create a Better Enterprise Search Engine
Keywords:
Enterprise Search Engine, Automated Machine Learning, AI Optimization, Search EfficiencyAbstract
Particularly as data volumes & user expectations rise, the area of corporate search engines has been steadily faced the challenge of producing search results that are both very relevant & the efficient. Often lacking the necessary degrees of customizing, precision & the speed modern businesses want, conventional search engine approaches fall short. The fast advancement in artificial intelligence (AI) has spawned a new field defined by neural architecture search (NAS) and automated machine learning (AutoML). The design and optimization of search engines might be transformed by these modern technologies. By independently choosing the best algorithms for a given application, AutoML simplifies the model choosing and training process. Concurrently, NAS stresses the automated identifications of the most effective neural network architectures. By enhancing the potential to adapt the results to the individual needs of consumers & the businesses, this combination may provide more effective search systems. While NAS improves the basic neural networks for better performances, AutoML lets search engines quickly change to fit changing data patterns & the user behaviors.
References
1. Boyan, J., Freitag, D., & Joachims, T. (1996, August). A machine learning architecture for optimizing web search engines. In AAAI Workshop on Internet Based Information Systems (pp. 1-8).
2. Sparks, E. R., Talwalkar, A., Haas, D., Franklin, M. J., Jordan, M. I., & Kraska, T. (2015, August). Automating model search for large scale machine learning. In Proceedings of the Sixth ACM Symposium on Cloud Computing (pp. 368-380).
3. McCallum, A. K., Nigam, K., Rennie, J., & Seymore, K. (2000). Automating the construction of internet portals with machine learning. Information Retrieval, 3, 127-163.
4. Yoganarasimhan, H. (2020). Search personalization using machine learning. Management Science, 66(3), 1045-1070.
5. Mukherjee, R., & Mao, J. (2004). Enterprise Search: Tough Stuff: Why is it that searching an intranet is so much harder than searching the Web?. Queue, 2(2), 36-46.
6. Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., & Le, Q. V. (2019). Mnasnet: Platform-aware neural architecture search for mobile. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2820-2828).
7. Xin, D., Wu, E. Y., Lee, D. J. L., Salehi, N., & Parameswaran, A. (2021, May). Whither automl? understanding the role of automation in machine learning workflows. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-16).
8. Luo, X., Liu, D., Kong, H., Huai, S., Chen, H., & Liu, W. (2022). Lightnas: On lightweight and scalable neural architecture search for embedded platforms. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 42(6), 1784-1797.
9. Zhang, W., Shen, Y., Lin, Z., Li, Y., Li, X., Ouyang, W., ... & Cui, B. (2022, April). Pasca: A graph neural architecture search system under the scalable paradigm. In Proceedings of the ACM Web Conference 2022 (pp. 1817-1828).
10. Willard, J., Jia, X., Xu, S., Steinbach, M., & Kumar, V. (2022). Integrating scientific knowledge with machine learning for engineering and environmental systems. ACM Computing Surveys, 55(4), 1-37.
11. Tyagi, A. K., & Chahal, P. (2020). Artificial intelligence and machine learning algorithms. In Challenges and applications for implementing machine learning in computer vision (pp. 188-219). IGI Global.
12. Wu, J., Yu, Y., Huang, C., & Yu, K. (2015). Deep multiple instance learning for image classification and auto-annotation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3460-3469).
13. Liu, Y., Wang, G., Ascoli, G. A., Zhou, J., & Liu, L. (2022). Neuron tracing from light microscopy images: automation, deep learning and bench testing. Bioinformatics, 38(24), 5329-5339.
14. Zhou, J., Velichkevich, A., Prosvirov, K., Garg, A., Oshima, Y., & Dutta, D. (2019). Katib: A Distributed General {AutoML} Platform on Kubernetes. In 2019 USENIX Conference on Operational Machine Learning (OpML 19) (pp. 55-57).
15. Karmaker, S. K., Hassan, M. M., Smith, M. J., Xu, L., Zhai, C., & Veeramachaneni, K. (2021). Automl to date and beyond: Challenges and opportunities. ACM Computing Surveys (CSUR), 54(8), 1-36.
16. Thumburu, S. K. R. (2022). Real-Time Data Transformation in EDI Architectures. Innovative Engineering Sciences Journal, 2(1).
17. Thumburu, S. K. R. (2022). Scalable EDI Solutions: Best Practices for Large Enterprises. Innovative Engineering Sciences Journal, 2(1).
18. Gade, K. R. (2022). Data Modeling for the Modern Enterprise: Navigating Complexity and Uncertainty. Innovative Engineering Sciences Journal, 2(1).
19. Gade, K. R. (2022). Migrations: AWS Cloud Optimization Strategies to Reduce Costs and Improve Performance. MZ Computing Journal, 3(1).
20. Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE RELIABILITY WITH ACID TRANSACTIONS.
21. Katari, A., & Vangala, R. Data Privacy and Compliance in Cloud Data Management for Fintech.
22. Komandla, V. Enhancing Product Development through Continuous Feedback Integration “Vineela Komandla”.
23. Komandla, V. Strategic Feature Prioritization: Maximizing Value through User-Centric Roadmaps.
24. Thumburu, S. K. R. (2021). Optimizing Data Transformation in EDI Workflows. Innovative Computer Sciences Journal, 7(1).
25. Thumburu, S. K. R. (2021). Integrating Blockchain Technology into EDI for Enhanced Data Security and Transparency. MZ Computing Journal, 2(1).
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
