E-TİCARETTE OPERASYONEL VERİMLİLİK VE STRATEJİK AVANTAJ İÇİN YAPAY ZEKÂ DESTEKLİ ÜRÜN KATEGORİZASYONU

Abstract

E-ticaret ekosisteminde milyonlarca ürün, dinamik envanterler ve çoklu satış kanalları nedeniyle manuel ürün kategorizasyonu sürdürülemez hale gelmiştir. Bu çalışma, operasyonel verimsizlikler, bilişsel önyargılar ve ölçeklenememe gibi kısıtları aşmak için yapay zekâ tabanlı otonom bir kategorizasyon sistemi geliştirmektedir. Problem alt başlıklara ayrılarak kavramsallaştırılmış ve çekirdekte dönüştürücü mimarisine dayalı hibrit bir model kullanılmıştır. Türkçe metinler için önceden eğitilmiş BERT modeli, hiyerarşik sınıflandırmaya uyarlanmış; ayrıca metinsel eşleşmenin olmadığı durumlarda anlamsal arama modeli, ontoloji eşleştirme görevini üstlenmiştir. İki e-ticaret platformundan elde edilen gerçek verilerle yapılan deneylerde model, daha önce görmediği test verisinde %88,51 doğruluk sağlamış ve manuel sürece kıyasla adam-saat maliyetini %99 azaltmıştır. Sonuçlar, sistemin operasyonel verimlilik, maliyet avantajı ve sürdürülebilir rekabet gücü sunduğunu göstermektedir.

Keywords

• E-ticaret
• ürün kategorizasyonu
• doğal dil işleme
• bert
• hiyerarşik sınıflandırma
• makine öğrenmesi
• otomasyon

AI-POWERED PRODUCT CATEGORIZATION FOR OPERATIONAL EFFICIENCY AND STRATEGIC ADVANTAGE IN E-COMMERCE

Abstract

In the e-commerce ecosystem, with millions of products, dynamic inventories, and multiple sales channels, manual product categorization has become unsustainable. This study develops an AI-based autonomous categorization system to overcome limitations such as operational inefficiencies, cognitive biases, and lack of scalability. The problem is conceptualized into sub-tasks, with a hybrid model based on transformer architecture at its core. A pre-trained BERT model for Turkish texts is fine-tuned for hierarchical classification, while a semantic search model handles ontology matching when direct text matches are absent. Experiments using real data from two e-commerce platforms show the model achieved 88.51% accuracy on unseen test data and reduced manual categorization labor costs by 99%. Results demonstrate the system not only improves efficiency and cost savings but also offers sustainable competitive advantage for e-commerce businesses.

Keywords

• E-commerce
• product categorization
• natural language processing
• bert
• hierarchical classification
• machine learning
• automation

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