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Journals / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi / 2026 / Cilt: 28 Sayı: 83

Automatic Feature Recommendation System for Job Postings Using Unsupervised Clustering and Association Rule Mining

Denetimsiz Kümeleme ve İlişkisel Kural Analizi Kullanılarak İş İlanları için Otomatik Özellik Öneri Sistemi

Feriştah Dalkılıç(DOKUZ EYLÜL ÜNİVERSİTESİ)0000-0001-7528-5109
Yunus Doğan(DOKUZ EYLÜL ÜNİVERSİTESİ)0000-0002-0353-5014
Recep Alp Kut(DOKUZ EYLÜL ÜNİVERSİTESİ)0000-0002-5781-334X
Kemal Can Kara0000-0001-5938-9899
Uygar Takazoğlu0000-0002-9276-5339
Pages
276–283
Publication Date
2026-05-31
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AI Summary & Key Takeaways

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

Our results demonstrate that using such a recommender system makes the job posting preparation process fast and easy and increases the quality of the job postings created.

📌 3 Key Takeaways

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Objective & Hypothesis:All sectors of business use online employment platforms to publish job postings to find the qualified personnel they need.
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Key Findings & Data:A job posting text should be neither long enough to make it difficult to read, nor short enough to create a feeling of carelessness, and should contain clear information about the position.
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Conclusion & Impact:Our results demonstrate that using such a recommender system makes the job posting preparation process fast and easy and increases the quality of the job postings created.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Yapay Zeka · İlişkisel Kural Analizi · Kümeleme Algoritmaları

Abstract

All sectors of business use online employment platforms to publish job postings to find the qualified personnel they need. A job posting text should be neither long enough to make it difficult to read, nor short enough to create a feeling of carelessness, and should contain clear information about the position. However, the preparation of a proper job posting text that complies with the qualifications needed by the job requires expertise and experience. Here, we propose a feature recommendation system to assist recruiters who prepare job posting text. The proposed system mines the relations between the features on a job posting dataset obtained from Kariyer.Net. We used text-mining and natural language processing techniques and employed the DBSCAN clustering algorithm to obtain feature clusters. In addition, we used the Apriori algorithm for finding association rules and frequent item sets to discover relations between feature clusters. Furthermore, we developed a user interface to experiment with feature suggestion results on selected job titles. Our results demonstrate that using such a recommender system makes the job posting preparation process fast and easy and increases the quality of the job postings created.

Özet

Tüm sektörlerdeki işletmeler, ihtiyaç duydukları nitelikli personeli bulmak için iş ilanları yayınlamak üzere çevrimiçi istihdam platformlarını kullanmaktadır. Bir iş ilanı metni, okunmasını zorlaştıracak kadar uzun veya özensizlik hissi uyandıracak kadar kısa olmamalı ve pozisyon hakkında net bilgiler içermelidir. Ancak, işin gerektirdiği niteliklere uygun, doğru bir iş ilanı metni hazırlamak uzmanlık ve deneyim gerektirir. Bu çalışmada, iş ilanı metni hazırlayan insan kaynakları uzmanlarına yardımcı olmak için bir özellik önerme sistemi geliştirilmesi hedeflenmiştir. Geliştirilen sistem ile, Kariyer.Net'ten alınan iş ilanı verisetindeki özellikler kümelenmiş ve aralarındaki ilişkiler incelenmiştir. Özellik kümelerinin oluşturulması için metin madenciliği ve doğal dil işleme tekniklerinden yararlanılmış ve DBSCAN kümeleme algoritması kullanılmıştır. Ayrıca, özellik kümeleri arasındaki ilişkileri keşfetmek, ilişki kurallarını ve birlikte sık kullanılan özellikleri belirlemek amacıyla Apriori algoritması tercih edilmiştir. Belirtilen iş alanına uygun özellik önerilerinin sunulması amacıyla bir kullanıcı arayüzü geliştirilmiştir. Sonuçlarımız, yapay zeka destekli bir öneri sisteminin kullanılmasının, iş ilanı hazırlama sürecini hızlı ve kolay hale getirdiğini ve oluşturulan iş ilanlarının kalitesini artırdığını göstermektedir.

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Cite This Article

Dalkılıç, F., Doğan, Y., Kut, R. A., Kara, K. C., Takazoğlu, U. (2026). Automatic Feature Recommendation System for Job Postings Using Unsupervised Clustering and Association Rule Mining. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, Cilt: 28 Sayı: 83, 276–283. https://doi.org/10.21205/deufmd.2026288313

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