Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems

Chenxi Shi, Penghao Liang, Yichao Wu, Tong Zhan, Zhengyu Jin
4/1/2024
cs.IRcs.AI

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

PerOS: Personalized Self-Adapting Operating Systems in the Cloud

84% match2024

Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models

83% match2025

Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations

83% match2024

Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models

82% match2024

Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning

82% match2024

From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations

82% match2024

Cite this paper

@article{shi2024maximizing,
  title  = {Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems},
  author = {Chenxi Shi and Penghao Liang and Yichao Wu and Tong Zhan and Zhengyu Jin},
  year   = {2024},
  eprint = {2404.00903},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.00903v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2404.00903.svg)](https://codeofpaper.com/paper/2404.00903)

Discussion