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Comparative Analysis of Machine Learning Models for Music Recommendation
- 1 Cornell University, NY, USA
- 2 University of California at Berkeley, CA, USA
- 3 University of Michigan, Ann Arbor, MI, USA
- 4 University of Illinois at Urbana-Champaign, IL, USA
- 5 The George Washington University, DC, USA
- 6 Tsinghua University, Beijing, China
* Author to whom correspondence should be addressed.
Abstract
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.
Keywords
Exploratory data analysis (EDA), K Nearest Neighbors (KNN), Random Forest, Light Gradient Boosting Machine (LightGBM), Music Recommendation.
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Cite this article
Cheng,X.;Liu,K.;Hu,X.;Liu,T.;Che,C.;Zhu,C. (2024). Comparative Analysis of Machine Learning Models for Music Recommendation. Theoretical and Natural Science,53,249-254.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Applied Physics and Mathematical Modeling
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