Investigation on the Self-Improving Algorithm of TikTok Based on Extensive User Interactions

Xiaoxing Chen

2024

Abstract

The ubiquity of short video apps in contemporary society is epitomized by the widespread adoption of TikTok on mobile devices. The platform's escalating user rates and engagement duration are indicative of its growing influence. This paper investigates the TikTok algorithm's ability to process extensive data sets to curate and recommend user-preferred content. Conducted through a series of surveys and analytical studies across various age demographics within university populations, this research emphasizes the pivotal role of metadata tags and the platform's autonomous enhancement algorithms.By harnessing advanced machine learning and artificial intelligence technologies—such as Graph Neural Networks (GNN), Reinforcement Learning (RL), Temporal Convolutional Networks (TCN), Natural Language Processing (NLP), Generative Adversarial Networks (GANs), and Attention Mechanisms—TikTok effectively tailors its algorithmic learning to user interactions. This strategic integration allows for the progressive refinement of user recommendations, fostering personalized content delivery while ensuring privacy, and enhancing the overall quality of content and user engagement. The study's findings reveal that these technological integrations enable TikTok to more accurately discern user preferences, thus facilitating the delivery of more engaging and relevant content. Ultimately, these improvements have substantial implications for the enrichment of user experience on the platform.

Paper Citation

in Harvard Style

Chen X. (2024). Investigation on the Self-Improving Algorithm of TikTok Based on Extensive User Interactions. 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 227-233. DOI: 10.5220/0012925100004508

in Bibtex Style

@conference{emiti24,
author={Xiaoxing Chen},
title={Investigation on the Self-Improving Algorithm of TikTok Based on Extensive User Interactions},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={227-233},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012925100004508},
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 - Investigation on the Self-Improving Algorithm of TikTok Based on Extensive User Interactions
SN - 978-989-758-713-9
AU - Chen X.
PY - 2024
SP - 227
EP - 233
DO - 10.5220/0012925100004508
PB - SciTePress