Adaptive Recommendation System Strategies: An Exploration of Online Machine Learning Algorithms

Fenglin Lu

2024

Abstract

This paper investigates the application of online machine learning algorithms in adaptive recommendation systems, focusing on the integration of reinforcement learning and multi-armed bandit (MAB) frameworks to enhance user experience and platform efficiency. As digital environments become increasingly dynamic, traditional recommendation systems face challenges such as the exploration-exploitation dilemma and the cold start problem. To address these issues, adaptive strategies that leverage the real-time decision-making capabilities of reinforcement learning and the optimization potential of MAB algorithms are explored. The research demonstrates how these technologies improve personalization and operational efficiency by dynamically adjusting to user preferences and behaviors. This paper serves as a valuable resource for individuals seeking to comprehend and explore the utilization of MAB algorithms in recommendation systems, while also shedding light on potential avenues for future research in the domain of online recommendation systems.

Paper Citation

in Harvard Style

Lu F. (2024). Adaptive Recommendation System Strategies: An Exploration of Online Machine Learning Algorithms. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 447-451. DOI: 10.5220/0012949200004508

in Bibtex Style

@conference{emiti24,
author={Fenglin Lu},
title={Adaptive Recommendation System Strategies: An Exploration of Online Machine Learning Algorithms},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={447-451},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012949200004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Adaptive Recommendation System Strategies: An Exploration of Online Machine Learning Algorithms
SN - 978-989-758-713-9
AU - Lu F.
PY - 2024
SP - 447
EP - 451
DO - 10.5220/0012949200004508
PB - SciTePress