Dergiler / Sakarya University Journal of Computer and Information Sciences / 2026 / Volume: 9 Issue: 3

Feature Extraction for Real Estate Images and Titles with LLMs

Gülfem Işıklar Alptekin(GALATASARAY UNIVERSITY, FACULTY OF ENGINEERING AND TECHNOLOGY, DEPARTMENT OF COMPUTER ENGINEERING)0000-0003-0146-1581
Günce Keziban Orman(GALATASARAY UNIVERSITY, FACULTY OF ENGINEERING AND TECHNOLOGY, DEPARTMENT OF COMPUTER ENGINEERING)0000-0003-0402-8417
Sayfa
—
Yayın Tarihi
2026-06-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Experimental results demonstrate that our framework achieves up to a 44.26% improvement in recall for listing attributes, such as the presence of a balcony or the furnishing status of a property.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Images and titles often contain rich latent information about their associated objects, particularly on web-based platforms.
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Temel Bulgular & Veri:The framework incorporates ReAct, LLM-as-a-Judge, and few-shot prompting techniques.
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Sonuç & Katkı:Experimental results demonstrate that our framework achieves up to a 44.26% improvement in recall for listing attributes, such as the presence of a balcony or the furnishing status of a property.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:CLIP · Feature extraction · Information retrieval

Abstract

Images and titles often contain rich latent information about their associated objects, particularly on web-based platforms. Real estate websites provide a clear example, where listing images and titles provide important details that assist users in their decision-making. However, these unstructured elements cannot be directly utilized in downstream machine learning tasks, since their contextual meaning is not directly interpretable. This work aims to transform listing images and titles into structured, tabular representations, making them suitable for analytical and predictive modeling. To this end, we propose a modular framework based on state-of-the-art large language models. The framework incorporates ReAct, LLM-as-a-Judge, and few-shot prompting techniques. Its performance is evaluated on a real-world real estate dataset and compared with BERT and CLIP-based baselines. Experimental results demonstrate that our framework achieves up to a 44.26% improvement in recall for listing attributes, such as the presence of a balcony or the furnishing status of a property.

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Bu Makaleye Atıf Yap

Arslan, A., Yetki, T. D., Yücel, A., Turgut, H., Bali, Ö., Alptekin, G. I., Orman, G. K. (2026). Feature Extraction for Real Estate Images and Titles with LLMs. Sakarya University Journal of Computer and Information Sciences, Volume: 9 Issue: 3. https://doi.org/10.35377/saucis...1829206

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