Within the contemporary digital landscape characterized by unprecedented informational deluge, short-form video platforms have profoundly reconfigured global users' paradigms for entertainment consumption and knowledge acquisition. This transformative impact stems from the medium's inherent attributes: temporal immediacy, content fragmentation, and profoundly immersive engagement. Nevertheless, amidst exponential expansion of user demographics and unprecedented diversification of content ecosystems, conventional recommendation algorithms persistently encounter substantive impediments. These manifest in deficiently capturing ephemeral content relevance, implementing granular operational differentiation across heterogeneous user segments, and unearthing latent correlations within complex behavioral sequences. Leveraging a comprehensive empirical dataset (encompassing 122,500 discrete behavioral traces) sourced from a preeminent short-video platform, this investigation employs multifaceted data excavation and sophisticated model architecture to elucidate profound interconnections between user behavioral archetypes and recommendation optimization strategies. The seminal contribution of this research transcends the traditional system's constrained "content-centric" paradigm. It pioneeringly amalgamates temporal dynamics, nuanced behavioral differentiation across cohorts, and adaptive feedback mechanisms within a cohesive analytical framework. Consequently, it furnishes both conceptual underpinnings and actionable methodologies for developing intelligent recommendation engines capable of delivering hyper-personalized experiences-effectively actualizing the vision of "contextually attuned personalization for diverse users across manifold temporal instances."
| Published in | Science Innovation (Volume 13, Issue 3) |
| DOI | 10.11648/j.si.20251303.16 |
| Page(s) | 47-56 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Short Video, Recommendation System, K-means Clustering, Machine Learning, Personalized Recommendation
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APA Style
Zaozhuang, S. (2025). Research on the Innovation and Optimization of the Short Video Recommendation System on a Certain Platform. Science Innovation, 13(3), 47-56. https://doi.org/10.11648/j.si.20251303.16
ACS Style
Zaozhuang, S. Research on the Innovation and Optimization of the Short Video Recommendation System on a Certain Platform. Sci. Innov. 2025, 13(3), 47-56. doi: 10.11648/j.si.20251303.16
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Download@article{10.11648/j.si.20251303.16,
author = {Sun Zaozhuang},
title = {Research on the Innovation and Optimization of the Short Video Recommendation System on a Certain Platform
},
journal = {Science Innovation},
volume = {13},
number = {3},
pages = {47-56},
doi = {10.11648/j.si.20251303.16},
url = {https://doi.org/10.11648/j.si.20251303.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.si.20251303.16},
abstract = {Within the contemporary digital landscape characterized by unprecedented informational deluge, short-form video platforms have profoundly reconfigured global users' paradigms for entertainment consumption and knowledge acquisition. This transformative impact stems from the medium's inherent attributes: temporal immediacy, content fragmentation, and profoundly immersive engagement. Nevertheless, amidst exponential expansion of user demographics and unprecedented diversification of content ecosystems, conventional recommendation algorithms persistently encounter substantive impediments. These manifest in deficiently capturing ephemeral content relevance, implementing granular operational differentiation across heterogeneous user segments, and unearthing latent correlations within complex behavioral sequences. Leveraging a comprehensive empirical dataset (encompassing 122,500 discrete behavioral traces) sourced from a preeminent short-video platform, this investigation employs multifaceted data excavation and sophisticated model architecture to elucidate profound interconnections between user behavioral archetypes and recommendation optimization strategies. The seminal contribution of this research transcends the traditional system's constrained "content-centric" paradigm. It pioneeringly amalgamates temporal dynamics, nuanced behavioral differentiation across cohorts, and adaptive feedback mechanisms within a cohesive analytical framework. Consequently, it furnishes both conceptual underpinnings and actionable methodologies for developing intelligent recommendation engines capable of delivering hyper-personalized experiences-effectively actualizing the vision of "contextually attuned personalization for diverse users across manifold temporal instances."},
year = {2025}
}
TY - JOUR T1 - Research on the Innovation and Optimization of the Short Video Recommendation System on a Certain Platform AU - Sun Zaozhuang Y1 - 2025/07/15 PY - 2025 N1 - https://doi.org/10.11648/j.si.20251303.16 DO - 10.11648/j.si.20251303.16 T2 - Science Innovation JF - Science Innovation JO - Science Innovation SP - 47 EP - 56 PB - Science Publishing Group SN - 2328-787X UR - https://doi.org/10.11648/j.si.20251303.16 AB - Within the contemporary digital landscape characterized by unprecedented informational deluge, short-form video platforms have profoundly reconfigured global users' paradigms for entertainment consumption and knowledge acquisition. This transformative impact stems from the medium's inherent attributes: temporal immediacy, content fragmentation, and profoundly immersive engagement. Nevertheless, amidst exponential expansion of user demographics and unprecedented diversification of content ecosystems, conventional recommendation algorithms persistently encounter substantive impediments. These manifest in deficiently capturing ephemeral content relevance, implementing granular operational differentiation across heterogeneous user segments, and unearthing latent correlations within complex behavioral sequences. Leveraging a comprehensive empirical dataset (encompassing 122,500 discrete behavioral traces) sourced from a preeminent short-video platform, this investigation employs multifaceted data excavation and sophisticated model architecture to elucidate profound interconnections between user behavioral archetypes and recommendation optimization strategies. The seminal contribution of this research transcends the traditional system's constrained "content-centric" paradigm. It pioneeringly amalgamates temporal dynamics, nuanced behavioral differentiation across cohorts, and adaptive feedback mechanisms within a cohesive analytical framework. Consequently, it furnishes both conceptual underpinnings and actionable methodologies for developing intelligent recommendation engines capable of delivering hyper-personalized experiences-effectively actualizing the vision of "contextually attuned personalization for diverse users across manifold temporal instances." VL - 13 IS - 3 ER -
School of Management, University of Shanghai for Science and Technology, Shanghai, China
