İnsan kaynakları süreçlerinde yapay zekâ modellerinin uygulanmasına bir örnek

Year 2024, , 101 - 116, 21.10.2024

Selim Sürücü , Berk Küçük , Mustafa Kemal Aydın

https://doi.org/10.33707/akuiibfd.1443940

Abstract

İş ilanı oluşturmak ve bu iş ilanları arasından uygun adayları seçmek zorlu bir süreçtir. Bu süreç insan kaynaklarının iş yükünü arttırmakta, sürecin yavaş ilerlemesine neden olmaktadır. İnsan kaynakları departmanlarının etkin bir şekilde iş ilanı oluşturabilmesi ve bu ilanlara yapılan başvuruların özgeçmişlerinin değerlendirilmesi süreçlerinde bilgi işlem teknolojilerinden yararlanılması büyük bir öneme sahiptir. Bu çalışma, insan kaynaklarına yardımcı olabilecek, iki farklı teknolojinin tanıtımını ve analizini yapmaktadır. Bilgi işlem teknolojileri alanında iş ilanlarının hazırlanması sürecinde, ilk aşamada kelime bulutu yöntemi kullanılarak ilan metinlerinde hangi anahtar kelimelerin vurgulanması gerektiğine karar verilir. İkinci aşamada, başvuran adayların özgeçmişleri, sınıflandırma amacıyla CNN (Convolutional Neural Network), GRU (Gated Recurrent Unit) ve LSTM (Long Short-Term Memory) gibi üç farklı derin öğrenme modeli kullanılarak analiz edilmiştir. Bu modellerin performansları, doğruluk, MCC, F_1 score ve MSE gibi metrikler kullanılarak değerlendirilirken, açıklanabilir yapay zekâ ile modellerin karar verme süreçleri de incelenmiştir. Bu çerçevede, %99'luk bir doğruluk başarısı sergileyen GRU modeli, bu çalışma kapsamında ve literatürde elde edilen en üstün sonucu sağlamıştır. Bu araştırma, derin öğrenme modellerinin, insan kaynakları alanında özgeçmiş sınıflandırma ve aday eşleştirme süreçlerinde yüksek doğruluk oranları ve verimlilik sağladığını göstermektedir. Ayrıca, kelime bulutu yöntemi kullanılarak en uygun anahtar kelimelerin belirlenerek ilanların oluşturulabileceğini de anlatmaktadır.

Keywords

Özgeçmiş sınıflandırma, Yapay zekâ destekli insan kaynakları otomasyonu, Öneri sistemi, Doğal dil işleme, Metin sınıflandırması

An example of the application of artificial intelligence models in human resources processes

Abstract

Creating job postings and selecting suitable candidates among these job postings is a challenging process. This process increases the workload of human resources and causes the process to proceed slowly. It is of great importance for human resources departments to utilize information processing technologies to create job postings effectively and to evaluate the CVs of applicants to these postings. This study introduces and analyzes two different technologies that can help human resources. In the process of preparing job advertisements in the field of IT, in the first stage, the word cloud method is used to decide which keywords should be emphasized in the advertisement texts. In the second stage, the resumes of the applicants are analyzed using three different deep learning models such as CNN (Convolutional Neural Network), GRU (Gated Recurrent Unit), and LSTM (Long Short-Term Memory) for classification purposes. While the performance of these models is evaluated using metrics such as accuracy, MCC, F_1 score, and MSE, the decision-making processes of the models with explainable artificial intelligence are also analyzed. In this context, the GRU model, which achieved an accuracy of 99%, provided the most superior result in this study and the literature. This research shows that deep learning models provide high accuracy rates and efficiency in human resources resume classification and candidate matching processes. It also explains that using the word cloud method, the most appropriate keywords can be identified, and advertisements can be created.

Keywords

Resume classification, AI-powered human resources automation, Recommender System, Natural Language Processing, Text Classification

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