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EVALUATION OF PERFORMANCE METRICS USED IN RECOMMENDATION SYSTEMS ACCORDING TO FILTERING TECHNOLOGIES: A RESEARCH STUDY ON THE FIELD OF JOB RECOMMENDATION SYSTEMS

Yıl 2024, Cilt: 27 Sayı: 3, 706 - 725, 03.09.2024

Selin Bitirim , Duygu Çelik Ertuğrul

https://doi.org/10.17780/ksujes.1410926

Öz

Thanks to Recommendation Systems (RSs), it has become possible to carry out existing processes/operations effectively in almost every sector (e.g. e-commerce, education, entertainment, healthcare, human resources, advertising, etc.) and to prioritize items that may interest the user. With the contribution of RSs, it is possible to effectively manage sectoral processes/services and produce personalized results for users. This study aims to review RS-related research, reveal a taxonomy of filtering techniques, and identify widely encountered performance metrics. In addition, Job Recommendation Systems, which are indispensable for Human Resources (HR) management, were chosen as the research area in this study and it was planned to determine performance metrics and item filtering approaches. Various studies from the literature on RS architecture and solutions, conducted between 2010 and 2023, were selected according to their relevance and reviewed. Filtering techniques in RSs are classified hierarchically and the majority evaluation metrics used in performance evaluations are determined and categorized. Additionally, the reflections of the gains learned from RSs on Job Recommendation Systems were investigated and RS solutions/metrics in the field of HR were presented. Finally, this study serves as a road map for researchers who want to conduct research, development and quality evaluations on RS solutions.

Anahtar Kelimeler

Recommender systems job recommender systems taxonomy filtering performance metrics

Kaynakça

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ÖNERİ SİSTEMLERİNDE KULLANILAN PERFORMANS METRİKLERİNİN FİLTRELEME TEKNOLOJİLERİNE GÖRE DEĞERLENDİRİLMESİ: İŞ ÖNERİ SİSTEMLERİ ALANI ÜZERİNE BİR ARAŞTIRMA ÇALIŞMASI

Yıl 2024, Cilt: 27 Sayı: 3, 706 - 725, 03.09.2024

Selin Bitirim , Duygu Çelik Ertuğrul

https://doi.org/10.17780/ksujes.1410926

Öz

Tavsiye Sistemleri (Recommendation Systems—RSs) sayesinde hemen hemen her sektörde (ör. e-ticaret, eğitim, eğlence, sağlık, insan kaynakları, reklamcılık, vb.) mevcut süreçlerin/operasyonların etkin bir biçimde yürütülebilmesi ve kullanıcının ilgisini çekebilecek öğelere öncelik verilmesi mümkün hale gelmiştir. RS'lerin katkısı ile, sektörel süreçlerin/hizmetlerin etkin şekilde yönetilmesi ve kullanıcılara kişiselleştirilmiş sonuçlar üretilmesi mümkündür. Bu çalışmada, RS ile ilgili araştırmaların gözden geçirilmesi, filtreleme teknikleri taksonomisinin ortaya çıkarılması ve geniş çapta rastlanan performans metriklerinin tespiti amaçlanmaktadır. Ayrıca, İnsan Kaynakları (İK) yönetiminin olmazsa olmazı olan İş Tavsiye Sistemleri bu çalışmada, araştırma sahası olarak seçilmiş olup performans metriklerinin ve öğe filtreleme yaklaşımlarının belirlenmesi planlanmıştır. RS mimarisi ve çözümleri üzerine, literatürden 2010-2023 yılları arasında yapılmış çeşitli çalışmalar ilgililik durumuna göre seçilmiş ve incelenmiştir. RS’lerde filtreleme teknikleri hiyerarşik olarak sınıflandırılmış ve performans değerlendirmelerinde kullanılan çoğunluk değerlendirme metrikleri saptanarak kategorize edilmiştir. Ayrıca, RS'lerden öğrenilen kazanımların İş Tavsiye Sistemleri’ndeki yansımaları araştırılmış ve IK alanındaki RS çözümleri/metrikleri ortaya konulmuştur. Son olarak, RS çözümleri üzerinde araştırma, geliştirme ve kalite değerlendirmeleri yapmak isteyen araştırmacılara, bu çalışmamız bir yol haritası niteliğindedir.

Anahtar Kelimeler

Tavsiye Sistemleri İş Tavsiye Sistemleri Taksonomi Filtreleme Performans Metrikleri

Etik Beyan

Etik onay beyanı: Bu çalışma için resmi onay gerekli değildir.

Destekleyen Kurum

DOĞU AKDENİZ ÜNİVERSİTESİ

Kaynakça

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  • Al-Otaibi, S., & Ykhlef, M. (2017). Hybrid immunizing solution for job recommender system. Frontiers of Computer Science, 11(3), 511-527. https://doi.org/10.1007/s11704-016-5241-z
  • Al-Shamri, M. Y. H. (2016). User profiling approaches for demographic recommender systems. Knowledge-Based Systems, 100, 175-187. https://doi.org/10.1016/j.knosys.2016.03.006
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  • Pan, Y., Zhang, Y., & Zhang, R. (2016). Combo-Recommendation Based on Potential Relevance of Items. In Asia-Pacific Web Conference (pp. 505-517). Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_55
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Toplam 77 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İş Süreçleri Yönetimi, Karar Desteği ve Grup Destek Sistemleri, Bilgi Sistemleri (Diğer), Performans Değerlendirmesi
Bölüm Bilgisayar Mühendisliği
Yazarlar

Selin Bitirim 0000-0002-3575-5855

Duygu Çelik Ertuğrul 0000-0003-1380-705X

Yayımlanma Tarihi 3 Eylül 2024
Gönderilme Tarihi 27 Aralık 2023
Kabul Tarihi 17 Şubat 2024
Yayımlandığı Sayı Yıl 2024Cilt: 27 Sayı: 3

Kaynak Göster

APA Bitirim, S., & Çelik Ertuğrul, D. (2024). ÖNERİ SİSTEMLERİNDE KULLANILAN PERFORMANS METRİKLERİNİN FİLTRELEME TEKNOLOJİLERİNE GÖRE DEĞERLENDİRİLMESİ: İŞ ÖNERİ SİSTEMLERİ ALANI ÜZERİNE BİR ARAŞTIRMA ÇALIŞMASI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(3), 706-725. https://doi.org/10.17780/ksujes.1410926
 Kapak Resmi İndir