Optimizing Energy Industry E-Commerce Data Storage with Distributed File Systems and Cloud Computing

Authors

  • Alexander Kristensen Finance, London School of Economics, LSE, London
  • Charlotte van der Berg Economics, Stockholm University, SU
  • Matthias Hofmann International Trade and Finance, Vienna University of Economics and Business, WU Wien

Keywords:

Recommender Systems (RecSys), Large Language Models (LLMs), Personalized Recommendations, Deep Neural Networks (DNNs)

Abstract

Recommender Systems (RecSys) are crucial in managing information overload and enhancing user satisfaction across various digital platforms, including e-commerce and entertainment. Evolving from traditional models to Deep Neural Networks (DNNs) and, more recently, Large Language Models (LLMs), these systems leverage sophisticated algorithms to analyze user behaviors and preferences. LLMs, such as GPT-4, are trained on extensive datasets to comprehend and generate natural language, significantly advancing their ability to deliver personalized recommendations. This tutorial explores the transformative impact of LLMs on RecSys, discussing their development, application in handling complex datasets, and the integration of contextual insights. Real-world examples illustrate how LLMs enhance recommendation accuracy and user experience, highlighting challenges and future directions in the field.

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Published

2024-06-30

How to Cite

Kristensen, A., Berg, C. van der, & Hofmann, M. (2024). Optimizing Energy Industry E-Commerce Data Storage with Distributed File Systems and Cloud Computing. Journal of Artificial Intelligence and Information, 1, 1–7. Retrieved from https://woodyinternational.com/index.php/jaii/article/view/30