Kullanıcı Tercihlerine Göre Şarkı Önerisi için LightGBM'nin Optimizasyonu

Yıl 2024, Cilt: 7 Sayı: 2, 56 - 65, 26.09.2024

Ömer Mintemur

https://doi.org/10.38016/jista.1401095

Öz

Müzik parçaları kesinlikle bireyin ruh halini anında etkileyebilecek dönüştürücü bir yeteneğe sahiptir. Günümüzde, büyük veri kesintisiz bir akış hızına sahiptir ve her saat yeni müzik parçaları üretilmektedir. Bir şarkının beğenilip beğenilemeyeceğini dinlemeden karar vermek kişi için çok zordur. Ayrıca müzik parçalarının üretim hızına yetişmek mümkün değildir. Ancak bu zor durum Makine Öğrenmesi yöntemleri kullanılarak kolaylaştırılabilir. Bu çalışmada, yeni bir veri seti sunulmuş ve şarkı önerisi problemi bir sınıflandırma problemi olarak ele alınmıştır. Diğer veri setlerinin aksine bu veri seti tamamen kullanıcılarının dinlendikleri şarkıyı beğenip beğenmemelerini dikkate alarak oluşturulmuştur. Makine Öğrenmesi algoritması olarak LightGBM kullanılmıştır ve bu algoritma Extra Tree and Random Forest algoritmalarıyla karşılaştırılmıştır. Bu algoritmalar üç tane sürü tabanlı optimizasyon algoritması (Grey Wolf, Whale ve Particle Swarm) ile optimize edilmiştir. Sonuçlar, yeni veri setinin öz niteliklerinin şarkının beğeni durumunu ayırt etmede başarılı olduğunu ortaya koymaktadır. Dahası, sonuçlar göz önüne alındığında, LightGBM algoritmasının diğer iki algoritmaya göre daha yüksek bir performans sergilediği gözlemlenmiştir.

Anahtar Kelimeler

LightGBM, Makine Öğrenmesi, Sınıflandırma, Sürü Tabanlı Optimizasyon

Optimization of LightGBM for Song Suggestion Based on Users’ Preferences

Öz

Undoubtedly, music possesses the transformative ability to instantly influence an individual's mood. In the era of the incessant flow of substantial data, novel music compositions surface on an hourly basis. It is impossible to know for an individual whether he/she will like the song or not before listening. Moreover, an individual cannot keep up with this flow. However, with the help of Machine Learning (ML) techniques, this process can be eased. In this study, a novel dataset is presented, and song suggestion problem was treated as a binary classification problem. Unlike other datasets, the presented dataset is solely based on users' preferences, indicating the likeness of a song as specified by the user. The LightGBM algorithm, along with two other ML algorithms, Extra Tree and Random Forest, is selected for comparison. These algorithms were optimized using three swarm-based optimization algorithms: Grey Wolf, Whale, and Particle Swarm optimizers. Results indicated that the attributes of the new dataset effectively discriminated the likeness of songs. Furthermore, the LightGBM algorithm demonstrated superior performance compared to the other ML algorithms employed in this study.

Anahtar Kelimeler

LightGBM, Machine Learning, Classification, Swarm Based Optimization

Kaynakça

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