Dynamic Product Categorization with Multi-Modal AI: Leveraging Transformer Architecture for Enhanced Commerce Intelligence

Authors

  • Sureshkumar Karuppuchamy

DOI:

https://doi.org/10.22399/ijcesen.4120

Keywords:

Multi-modal Artificial Intelligence, Transformer Architecture, Product Categorization, Semantic Search Optimization, E-commerce Personalization, Content-Based Feature Extraction

Abstract

Product categorization using multi-modal artificial intelligence represents a significant advancement in e-commerce infrastructure, transforming how digital commerce platforms organize, classify, and present products to consumers. The integration of transformer-based architectures with comprehensive content analysis enables simultaneous processing of text descriptions, images, and videos to create powerful product understanding systems. Advanced feature extraction techniques leverage natural language processing, computer vision, and temporal analysis to capture meaningful product attributes that manual categorization processes often overlook. Implementation approaches using distributed processing architectures and lambda models demonstrate superior scalability while meeting real-time performance requirements typical of modern commerce platforms. Attention-based fusion of multiple data modalities reveals complex product relationships and consumer preference patterns beyond the capabilities of single-input systems. Enhanced search functionality emerges through semantic understanding capabilities that align user intent with product characteristics across diverse query types and interaction patterns. Personalized recommendation mechanisms benefit from rich categorical data to deliver targeted content that resonates with individual consumer preferences and behavioral patterns. This technological advancement represents a fundamental shift from labor-intensive manual tagging systems toward intelligent automation that adapts to evolving product catalogs and consumer requirements. Commercial implementations demonstrate substantial improvements in search relevance, user engagement, and conversion rates across diverse retail environments. The comprehensive framework establishes new benchmarks for product discovery and recommendation systems in digital commerce platforms.

References

[1] Claudimar Pereira da Veiga et al., "E-Commerce in Brazil: An In-Depth Analysis of Digital Growth and Strategic Approaches for Online Retail," MDPI, 2024. [Online]. Available: https://www.mdpi.com/0718-1876/19/2/76

[2] Gabriel de Souza P. Moreira et al., "Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation," arXiv, 2021. [Online]. Available: https://arxiv.org/pdf/2107.05124

[3] Weiguo Feng et al., "Research on the construction and application of an intelligent tutoring system for English teaching based on a generative pre-training model," ScienceDirect, 2025. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S277294192500050X

[4] Alexey Dosovitskiy et al., "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale," ICLR, 2021. [Online]. Available: https://arxiv.org/pdf/2010.11929/1000

[5] HUAQIAN HE et al., "Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis Method," IEEE Access, 2022. [Online]. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9919154

[6] Ye Bi et al., "A Multimodal Late Fusion Model for E-Commerce Product Classification," arXiv, 2020. [Online]. Available: https://arxiv.org/pdf/2008.06179

[7] Gautam Pal et al., "Multi-Agent Big-Data Lambda Architecture Model for E-Commerce Analytics," MDPI, 2018. [Online]. Available: https://www.mdpi.com/2306-5729/3/4/58

[8] Yixuan Wu et al., "OmniFuse: A general modality fusion framework for multi-modality learning on low-quality medical data," ScienceDirect, 2025. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1566253524006687

[9] Han Zhang et al., "Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning," arXiv, 2020. [Online]. Available: https://arxiv.org/pdf/2006.02282

[10] Xiaodong Zhang et al., "Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data," MDPI, 2024. [Online]. Available: https://www.mdpi.com/2076-3417/14/18/8181

Downloads

Published

2025-10-16

How to Cite

Sureshkumar Karuppuchamy. (2025). Dynamic Product Categorization with Multi-Modal AI: Leveraging Transformer Architecture for Enhanced Commerce Intelligence. International Journal of Computational and Experimental Science and Engineering, 11(4). https://doi.org/10.22399/ijcesen.4120

Issue

Section

Research Article