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Journals / Advances in Artificial Intelligence Research / 2026 / Cilt: 5 Sayı: 1

Hybrid Visual-Textual Product Recommendation System for E-Commerce Platforms

Pınar Süngü İşiaçik0000-0002-9234-8586
Onur Tunali0000-0002-2326-0708
Emre Tekelioğlu0009-0001-7619-1267
Ali Hakan Işik(Burdur Mehmet Akif Ersoy Üniversitesi)0000-0003-3561-9375
Pages
1–6
Publication Date
2025-06-16
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The system has been successfully deployed on the Cimri e-commerce platform, providing personalized recommendations that adapt to evolving user preferences while maintaining computational efficiency.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Today, e-commerce sites provide a large number of products to users.
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Key Findings & Data:This study presents a novel hybrid product recommendation system that integrates collaborative filtering and content-based filtering methods, enhanced by deep learning techniques.
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Conclusion & Impact:The system has been successfully deployed on the Cimri e-commerce platform, providing personalized recommendations that adapt to evolving user preferences while maintaining computational efficiency.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Ürün Öneri Sistemi · E-Ticaret · İşbirlikçi Filtreleme

Abstract

Today, e-commerce sites provide a large number of products to users. However, presenting the right products to users is important for both customer satisfaction and increasing company revenues. Recommendation systems are systems that offer personalized product suggestions by analyzing user preferences and behaviors. This study presents a novel hybrid product recommendation system that integrates collaborative filtering and content-based filtering methods, enhanced by deep learning techniques. By using both visual and textual product features through BERT and CLIP models, our system addresses cold-start problem and real-time performance constraints. The system has been successfully deployed on the Cimri e-commerce platform, providing personalized recommendations that adapt to evolving user preferences while maintaining computational efficiency.

Özet

Günümüzde e-ticaret siteleri çok sayıda ürünü kullanıcılara sunmaktadır. Ancak kullanıcılara doğru ürünlerin sunulması hem müşteri memnuniyeti hem de şirket gelirlerinin artırılması için önemlidir. Öneri sistemleri, kullanıcı tercihlerini ve davranışlarını analiz ederek kişiselleştirilmiş ürün önerileri sunan sistemlerdir. Bu çalışma, işbirlikçi filtreleme ve içerik tabanlı filtreleme yöntemlerini bütünleştiren, derin öğrenme teknikleriyle geliştirilmiş yeni bir hibrit ürün öneri sistemi sunmaktadır. BERT ve CLIP modelleri aracılığıyla hem görsel hem de metinsel ürün özelliklerini kullanan sistemimiz, soğuk başlatma sorununu ve gerçek zamanlı performans kısıtlamalarını ele almaktadır. Sistem, Cimri e-ticaret platformunda başarıyla uygulanmış olup, hesaplama verimliliğini korurken gelişen kullanıcı tercihlerine uyum sağlayan kişiselleştirilmiş öneriler sunmaktadır.

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Cite This Article

Tekelioğlu, E., Işik, A. H. (2025). Hybrid Visual-Textual Product Recommendation System for E-Commerce Platforms. Advances in Artificial Intelligence Research, Cilt: 5 Sayı: 1, 1–6. https://doi.org/10.54569/aair.1700682

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