Download pdf
Maximizing user experience with LLMOps-driven personalized recommendation systems
- 1 Software development ,Telecommunication Systems Management ,Northeastern University
- 2 Information Systems, Northeastern University
- 3 Computer Science,Northeastern University
- 4 Computer Science,Columbia University
- 5 Informatics,Univeristy of California
* Author to whom correspondence should be addressed.
Abstract
The integration of LLMOps into personalized recommendation systems marks a significant advancement in managing LLM-driven applications. This innovation presents both opportunities and challenges for enterprises, requiring specialized teams to navigate the complexity of engineering technology while prioritizing data security and model interpretability. By leveraging LLMOps, enterprises can enhance the efficiency and reliability of large-scale machine learning models, driving personalized recommendations aligned with user preferences. Despite ethical considerations, LLMOps is poised for widespread adoption, promising more efficient and secure machine learning services that elevate user experience and shape the future of personalized recommendation systems.
Keywords
Personalized recommendation system, Artificial intelligence, LLMOps, User experience
[1]. Zhao, H., Si, L., Li, X., & Zhang, Q. (2017). Recommending Complementary Products in E-Commerce Push Notifications with a Mixture Model Approach. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval.
[2]. Fang, C., Li, X., Fan, Z., Xu, J., Nag, K., Korpeoglu, E., Kumar, S., & Achan, K. (2024). LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction.
[3]. Kulkarni, Akshay, et al. "LLMs for Enterprise and LLMOps." Applied Generative AI for Beginners: Practical Knowledge on Diffusion Models, ChatGPT, and Other LLMs. Berkeley, CA: Apress, 2023. 117-154.
[4]. Arawjo, I., Swoopes, C., Vaithilingam, P., Wattenberg, M., & Glassman, E. (2023). ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing. arXiv preprint arXiv:2309.09128.
[5]. D. Hershey and D. Oppenheimer (2023).. Development tools for language models - Predicting the future
[6]. Wan, Weixiang, et al. "Progress in artificial intelligence applications based on the combination of self-driven sensors and deep learning." arXiv preprint arXiv:2402.09442 (2024).
[7]. Wang, Yong, et al. "Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data." arXiv preprint arXiv:2402.15796 (2024).
[8]. Zheng, Jiajian, et al. "The Random Forest Model for Analyzing and Forecasting the US Stock Market in the Context of Smart Finance." arXiv preprint arXiv:2402.17194 (2024).
[9]. Yang, Le, et al. "AI-Driven Anonymization: Protecting Personal Data Privacy While Leveraging Machine Learning." arXiv preprint arXiv:2402.17191 (2024).
[10]. Cheng, Qishuo, et al. "Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis." arXiv preprint arXiv:2402.15994 (2024).
[11]. Zhu, Mengran, et al. "Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data." arXiv preprint arXiv:2402.09830 (2024).
[12]. Wu, Jiang, et al. "Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models." arXiv preprint arXiv:2402.12916 (2024).
[13]. Yu, Hanyi, et al. "Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving." arXiv preprint arXiv:2402.16036 (2024).
[14]. Huo, Shuning, et al. "Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Research." arXiv preprint arXiv:2402.16038 (2024).
[15]. K. Tan and W. Li, "Imaging and Parameter Estimating for Fast Moving Targets in Airborne SAR," in IEEE Transactions on Computational Imaging, vol. 3, no. 1, pp. 126-140, March 2017, doi: 10.1109/TCI.2016.2634421.
[16]. K. Tan and W. Li, "A novel moving parameter estimation approach offast moving targets based on phase extraction," 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada, 2015, pp. 2075-2079, doi: 10.1109/ICIP.2015.7351166.
[17]. K. Xu, X. Wang, Z. Hu and Z. Zhang, "3D Face Recognition Based on Twin Neural Network Combining Deep Map and Texture," 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi'an, China, 2019, pp. 1665-1668, doi: 10.1109/ICCT46805.2019.8947113.
[18]. Shi, Peng, Yulin Cui, Kangming Xu, Mingmei Zhang, and Lianhong Ding. 2019. "Data Consistency Theory and Case Study for Scientific Big Data" Information 10, no. 4: 137. https://doi.org/10.3390/info10040137.
Cite this article
Shi,C.;Liang,P.;Wu,Y.;Zhan,T.;Jin,Z. (2024).Maximizing user experience with LLMOps-driven personalized recommendation systems.Applied and Computational Engineering,64,100-106.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content
About volume
Volume title: Proceedings of the 6th International Conference on Computing and Data Science
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).