Enhancing Recommendation Systems with Stochastic Processes and Reinforcement Learning

Sijie Dai

2024

Abstract

In the fast-paced domain of social media, the effectiveness of recommendation systems is crucial for maintaining high user engagement. Traditional approaches often fail to keep up with the dynamic and stochastic nature of user preferences, resulting in sub-optimal content personalization. This paper introduces an innovative approach by integrating stochastic processes with reinforcement learning to significantly enhance the adaptive capabilities of these systems, with a specific focus on TikTok's recommendation engine. The methodology leverages real-time user interactions and sophisticated machine learning algorithms to dynamically evolve and better align with user behavior. Extensive simulations were conducted within a modeled TikTok environment and the approach was compared with existing algorithms. The enhancements in the system's adaptability not only showed higher precision in content recommendation but also tailored engagement strategies that are responsive to shifting user interests. This approach not only underscores the potential for more nuanced user interaction models but also sets the groundwork for extending these techniques to other digital platforms, potentially transforming how content is curated and consumed in digital ecosystems.

Paper Citation

in Harvard Style

Dai S. (2024). Enhancing Recommendation Systems with Stochastic Processes and Reinforcement Learning. 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 603-608. DOI: 10.5220/0012960300004508

in Bibtex Style

@conference{emiti24,
author={Sijie Dai},
title={Enhancing Recommendation Systems with Stochastic Processes and Reinforcement Learning},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={603-608},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012960300004508},
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 - Enhancing Recommendation Systems with Stochastic Processes and Reinforcement Learning
SN - 978-989-758-713-9
AU - Dai S.
PY - 2024
SP - 603
EP - 608
DO - 10.5220/0012960300004508
PB - SciTePress