(Universitas Negeri Malang, and
Institut Sains dan Teknologi Terpadu Surabaya, Indonesia)(2) * Anik Nur Handayani
(Universitas Negeri Malang, Indonesia)(3) Heru Wahyu Herwanto
(Universitas Negeri Malang, Indonesia)(4) Yosi Kristian
(Institut Sains dan Teknologi Terpadu Surabaya, Indonesia)*corresponding author
AbstractManaging large and constantly evolving product catalogs is a significant challenge for e-commerce platforms, especially when visually similar products cannot be reliably distinguished using text-based methods. This study proposes a product grouping method that combines a fine-tuned EfficientNetV2M model with an adaptive Agglomerative Clustering strategy. Unlike conventional CNN-based approaches, which have limited scalability and a fixed number of clusters, the proposed method dynamically adjusts similarity thresholds and automatically forms clusters for unseen product variations. By linking deep visual feature extraction with adaptive clustering, the method enhances flexibility in handling product diversity. Experiments on the Shopee product image dataset show that it achieves a high Normalized Mutual Information (NMI) score of 0.924, outperforming standard baselines. These results demonstrate the method’s effectiveness in automating catalog organization and offer a scalable solution for inventory management and personalized recommendations in e-commerce platforms.
KeywordsProduct Grouping; E-commerce; Convolutional neural network; Agglomerative clustering; Normalized mutual information
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DOIhttps://doi.org/10.26555/ijain.v11i3.1979 |
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