Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems
<|reference_start|>Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems: 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.<|reference_end|>
@article{shi2024maximizing,
    title={Maximizing User Experience with LLMOps-Driven Personalized
  Recommendation Systems},
    author={Chenxi Shi, Penghao Liang, Yichao Wu, Tong Zhan, Zhengyu Jin},
    journal={arXiv preprint arXiv:2404.00903},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00903},
    primaryClass={cs.IR cs.AI}
}