Version 1
: Received: 28 July 2024 / Approved: 30 July 2024 / Online: 31 July 2024 (03:05:47 CEST)
How to cite:
Kristensen, A.; van der Berg, C.; Hofmann, M. Harnessing Large Language Models for Next-Generation Recommender Systems. Preprints2024, 2024072484. https://doi.org/10.20944/preprints202407.2484.v1
Kristensen, A.; van der Berg, C.; Hofmann, M. Harnessing Large Language Models for Next-Generation Recommender Systems. Preprints 2024, 2024072484. https://doi.org/10.20944/preprints202407.2484.v1
Kristensen, A.; van der Berg, C.; Hofmann, M. Harnessing Large Language Models for Next-Generation Recommender Systems. Preprints2024, 2024072484. https://doi.org/10.20944/preprints202407.2484.v1
APA Style
Kristensen, A., van der Berg, C., & Hofmann, M. (2024). Harnessing Large Language Models for Next-Generation Recommender Systems. Preprints. https://doi.org/10.20944/preprints202407.2484.v1
Chicago/Turabian Style
Kristensen, A., Charlotte van der Berg and Matthias Hofmann. 2024 "Harnessing Large Language Models for Next-Generation Recommender Systems" Preprints. https://doi.org/10.20944/preprints202407.2484.v1
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.
Keywords
Recommender Systems (RecSys); Large Language Models (LLMs);Personalized Recommendations; Deep Neural Networks (DNNs)
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
