Kullanıcı Tercihlerine Göre Şarkı Önerisi için LightGBM'nin Optimizasyonu

Year 2024, Volume: 7 Issue: 2, 56 - 65, 26.09.2024

Ömer Mintemur

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

Abstract

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.

Keywords

LightGBM, Makine Öğrenmesi, Sınıflandırma, Sürü Tabanlı Optimizasyon

Optimization of LightGBM for Song Suggestion Based on Users’ Preferences

Abstract

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.

Keywords

LightGBM, Machine Learning, Classification, Swarm Based Optimization

References

• Bartolomeo, P., 2022. Can music restore brain connectivity in post-stroke cognitive deficits? Med. Hypotheses 159, 110761.
• Benbouhenni, H., Hamza, G., Oproescu, M., Bizon, N., Thounthong, P., Colak, I., 2024. Application of fractional-order synergetic-proportional integral controller based on PSO algorithm to improve the output power of the wind turbine power system. Sci. Rep. 14, 609. https://doi.org/10.1038/s41598-024-51156-x
• Bottou, L., 2012. Stochastic Gradient Descent Tricks, in: Montavon, G., Orr, G.B., Müller, K.-R. (Eds.), Neural Networks: Tricks of the Trade, Lecture Notes in Computer Science. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 421–436. https://doi.org/10.1007/978-3-642-35289-8_25
• Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P., 2002. SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357.
• Farajzadeh, N., Sadeghzadeh, N., Hashemzadeh, M., 2023. PMG-Net: Persian music genre classification using deep neural networks. Entertain. Comput. 44, 100518.
• Fernández, A., Garcia, S., Herrera, F., Chawla, N.V., 2018. SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J. Artif. Intell. Res. 61, 863–905.
• Gentili, G., Simonutti, L., Struppa, D.C., 2023. Music: numbers in motion.
• Geurts, P., Ernst, D., Wehenkel, L., 2006. Extremely randomized trees. Mach. Learn. 63, 3–42. https://doi.org/10.1007/s10994-006-6226-1
• Gharehchopogh, F.S., Gholizadeh, H., 2019. A comprehensive survey: Whale Optimization Algorithm and its applications. Swarm Evol. Comput. 48, 1–24.
• Hawkins, V., 2022. Music-Color Synesthesia: A Historical and Scientific Overview. Aisthesis Honors Stud. J. 13.
• Hızlısoy, S., Arslan, R.S., Çolakoğlu, E., 2023. Music Genre Recognition Based on Hybrid Feature Vector with Machine Learning Methods. Çukurova Üniversitesi Mühendis. Fakültesi Derg. 38, 739–750.
• Ho, T.K., 1995. Random decision forests, in: Proceedings of 3rd International Conference on Document Analysis and Recognition. IEEE, pp. 278–282.
• Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.-Y., 2017. Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30.
• Kennedy, J., Eberhart, R., 1995. Particle swarm optimization, in: Proceedings of ICNN’95-International Conference on Neural Networks. IEEE, pp. 1942–1948.
• Kingma, D.P., Ba, J., 2017. Adam: A Method for Stochastic Optimization. https://doi.org/10.48550/arXiv.1412.6980
• Kohavi, R., 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection, in: Ijcai. Montreal, Canada, pp. 1137–1145.
• Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J., 2021. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst.
• Liu, Z., Xu, W., Zhang, W., Jiang, Q., 2023. An emotion-based personalized music recommendation framework for emotion improvement. Inf. Process. Manag. 60, 103256.
• Logan, B., 2000. Mel frequency cepstral coefficients for music modeling., in: Ismir. Plymouth, MA, p. 11.
• Loukas, S., Lordier, L., Meskaldji, D., Filippa, M., Sa De Almeida, J., Van De Ville, D., Hüppi, P.S., 2022. Musical memories in newborns: A resting‐state functional connectivity study. Hum. Brain Mapp. 43, 647–664. https://doi.org/10.1002/hbm.25677
• Mirjalili, S., Lewis, A., 2016. The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67.
• Mirjalili, S., Mirjalili, S.M., Lewis, A., 2014. Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61.
• Noble, W.S., 2006. What is a support vector machine? Nat. Biotechnol. 24, 1565–1567. Păvăloaia, V.-D., Necula, S.-C., 2023. Artificial intelligence as a disruptive technology—a systematic literature review. Electronics 12, 1102.
• Prabhakar, S.K., Lee, S.-W., 2023. Holistic approaches to music genre classification using efficient transfer and deep learning techniques. Expert Syst. Appl. 211, 118636.
• Risse, M., 2023. Political Theory of the Digital Age: Where Artificial Intelligence Might Take Us. Cambridge University Press.
• Saheed, Y.K., Misra, S., 2024. A voting gray wolf optimizer-based ensemble learning models for intrusion detection in the Internet of Things. Int. J. Inf. Secur. https://doi.org/10.1007/s10207-023-00803-x
• Singh, Y., Biswas, A., 2023. Lightweight convolutional neural network architecture design for music genre classification using evolutionary stochastic hyperparameter selection. Expert Syst. 40, e13241. https://doi.org/10.1111/exsy.13241
• Soekarta, R., Aras, S., Aswad, A.N., 2023. Hyperparameter Optimization of CNN Classifier for Music Genre Classification. J. RESTI Rekayasa Sist. Dan Teknol. Inf. 7, 1205–1210.
• Wen, Z., Chen, A., Zhou, G., Yi, J., Peng, W., 2024. Parallel attention of representation global time–frequency correlation for music genre classification. Multimed. Tools Appl. 83, 10211–10231.
• Wijaya, N.N., Muslikh, A.R., 2024. Music-Genre Classification using Bidirectional Long Short-Term Memory and Mel-Frequency Cepstral Coefficients. J. Comput. Theor. Appl. 2, 13–26.
• Yılmaz, P., Akçakaya, Ş., Özkaya, Ş.D., Çetin, A., 2022. Machine Learning Based Music Genre Classification and Recommendation System. El-Cezeri 9, 1560–1571.
• Yuwono, A., Tjiandra, C.A., Owen, C., Manuaba, I.B.K., 2023. Music Genre Classification Using Support Vector Machine Techniques, in: 2023 International Conference on Information Management and Technology (ICIMTech). IEEE, pp. 511–516.
• Zhao, J., Zhao, M., Yang, X., Li, X., Chen, Z., 2023. Music Style Recognition Method Based on Computer-Aided Technology for Internet of Things.