mlsanityData to 7 Oct 2026

Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems

arXiv 2404.00903 ↗First posted 1 Apr 2024cs.IR cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 12 of its 17 references that are in the map. First posted on arXiv 1 Apr 2024.

Builds on

12 of 17 references.

References · 12

  1. 2023ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testingcited by 88
  2. 2024LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extractioncited by 39
  3. 2017Recommending Complementary Products in E-Commerce Push Notifications with a Mixture Model Approachcited by 6
  4. 2024Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Datacited by 5
  5. 2024Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Modelscited by 5
  6. 2024Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysiscited by 5
  7. 2024The Random Forest Model for Analyzing and Forecasting the US Stock Market in the Context of Smart Financecited by 5
  8. 2024Progress in artificial intelligence applications based on the combination of self-driven sensors and deep learningcited by 4
  9. 2024Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS datacited by 4
  10. 2024AI-Driven Anonymization: Protecting Personal Data Privacy While Leveraging Machine Learningcited by 4
  11. 2024Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Drivingcited by 3
  12. 2024Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Researchcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2025Mind the Metrics: Patterns for Telemetry-Aware In-IDE AI Application Development using the Model Context Protocol (MCP)cited by 4
  2. 2024VickreyFeedback: Cost-efficient Data Construction for Reinforcement Learning from Human Feedbackcited by 2
  3. 2026Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logisticscited by 0