Enhancing Energy Sector E-Commerce Data Storage through Distributed File Systems and Cloud Solutions

Authors

  • Alexander Kristensen London School of Economics
  • Charlotte Van Der Berg Vienna University of Economics and Business

DOI:

https://doi.org/10.5281/zenodo.12747432

ARK:

https://n2t.net/ark:/40704/AJSM.v2n4a06

Keywords:

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

Abstract

Recommender Systems (RecSys) play a crucial role 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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Author Biographies

Alexander Kristensen, London School of Economics

Finance, London School of Economics, LSE, London.

Charlotte Van Der Berg, Vienna University of Economics and Business

International Trade and Finance, Vienna University of Economics and Business, WU Wien.

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Published

2024-07-18

How to Cite

Kristensen, A., & Van Der Berg, C. (2024). Enhancing Energy Sector E-Commerce Data Storage through Distributed File Systems and Cloud Solutions. Academic Journal of Sociology and Management, 2(4), 35–40. https://doi.org/10.5281/zenodo.12747432

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