Comparative Analysis of Machine Learning Models for Music Recommendation
Xi Cheng, Ke Liu, Xin Hu, Tong Liu, Chang Che, Chen Zhu · Theoretical and Natural Science · 2024
This study is inspired by the Kaggle competition “WSDM - KKBox’s Music Recommendation Challenge”. The study focuses on doing a comparative study on music recommendation models. Based on the requirements and the given dataset from the Kaggle competition, the problem can be transferred to a classification problem, and therefore, we chose three classification models for the comparative study. The three models are K Nearest Neighbors (KNN), Random Forest, and Light Gradient Boosting Machine (LightGBM). We also did data analysis and data preparation before applying the model and used the handout method and cross-validation method for the validation. For the evaluation, the AUC score is applied to the results and the empirical results show that LightGBM is the best model among these three.