Few-Shot and Domain Adaptation Modeling for Evaluating Growth Strategies in Long-Tail Small and Medium-sized Enterprises

Wenwen Liu

doi:10.70393/6a69656173.333434

OPEN ACCESS |Research Article ||3 December 2025

Few-Shot and Domain Adaptation Modeling for Evaluating Growth Strategies in Long-Tail Small and Medium-sized Enterprises

Wenwen Liu

University of Washington

liuwenwen.jessica@bytedance.com

University of Washington, USA.

* Corresponding Author¹: Wenwen Liu, E-Mail: liuwenwen.jessica@bytedance.com

Publication

Accepted 2025 November 20 ; Published 2025 December 3

Journal of Industrial Engineering and Applied Science, 2025, 3(6), 3005-6071.

Abstract

To enhance the execution of growth strategies for SMEs under data sparsity and domain shift, this study combines domain adaptation with few-shot learning to identify growth bottlenecks and generate actionable strategies through model optimization. Practical case studies validate the model's applicability and adaptability across domains. By integrating feature alignment and reweighting mechanisms, the strategy significantly improves performance in long-tail categories and cross-domain transferability.

Keywords

SMEs , Growth Strategies , Domain Adaptation , Few-shot Learning , Strategy Optimization .

Metadata

DOI:

10.70393/6a69656173.333434

ARK:

ark:/40704/JIEAS.v3n6a05

Pages: 30-35

References: 5

Disciplines: Artificial Intelligence Technology

Subjects: Machine Learning

Cite This Article

APA Style

Liu, W. (2025). Few-shot and domain adaptation modeling for evaluating growth strategies in long-tail small and medium-sized enterprises. Journal of Industrial Engineering and Applied Science, 3(6), 30-35. https://doi.org/10.70393/6a69656173.333434

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

References

1.

Zhang, Y., Zhang, M., & Liu, W. (2025). Joint distribution domain adaptation: A novel meta-learning framework for cross-domain few-shot fault diagnosis. Journal of Intelligent Manufacturing, Advance online publication, 1–22. https://doi.org/10.1007/s10845-025-02708-z

2.

Zou, Y., Gu, C., Yu, J., et al. (2025). Incremental pseudo-labeling for black-box unsupervised domain adaptation. Journal of Visual Communication and Image Representation, 113, 104630. https://doi.org/10.1016/j.jvcir.2025.104630

3.

Chen, Y., Zhu, X., Li, Y., et al. (2026). Contrast and clustering: Learning neighborhood pair representation for source-free domain adaptation. Signal Processing: Image Communication, 140, 117429. https://doi.org/10.1016/j.image.2025.117429

4.

Wang, S., Fu, Y., & Kim, J. (2026). Toward construction-specialized, small language models: The interplay of domain adaptation, model scale and data volume. Advanced Engineering Informatics, 69, 104035. https://doi.org/10.1016/j.aei.2025.104035

5.

Zhang, W., Ye, P., Chen, D., et al. (2026). ADA framework for unsupervised domain adaptation person re-identification. Pattern Recognition, 171, 112238. https://doi.org/10.1016/j.patcog.2025.112238

PUBLISHER'S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.