Music Recommendation System Using Alternating Least Squares Method

Authors

  • Muhammad Rafi Irfansyah Telkom University
  • Dade Nurjanah Telkom University
  • Hani Nurrahmi Telkom University

DOI:

https://doi.org/10.34818/INDOJC.2024.9.1.908

Keywords:

alternating least squares, collaborative filtering, sparse, recommendation system, music

Abstract

Music is not just entertainment, but it also has a positive impact on psychological well-being. The music landscape is generally dominated by millennials, especially in Indonesia. Music recommendation systems are becoming an important factor in offering songs that match users' preferences. Collaborative Filtering (CF), particularly the Alternating Least Squares (ALS) method, has become a popular solution for data sparsity problems in user-item interactions. Using the Precision@K metric, ALS provides the best results at a 50:50 data split ratio, 0.30225 for the Last FM dataset and 0.19742 for the Taste Profile dataset.

Further analysis shows that ALS is more effective on datasets with balanced data distributions, such as Last FM, than on datasets with noisier characteristics, such as Taste Profile. The main conclusion is that ALS is suitable for use on datasets with balanced data distributions and can provide more optimal recommendations. For further development, handling sparsity data on Taste Profile needs to be improved to improve the performance of the recommendation model. This illustrates the importance of adapting the model to the unique characteristics of each dataset to achieve more accurate music recommendations.

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Published

2024-05-13

How to Cite

Irfansyah, M. R., Dade Nurjanah, & Hani Nurrahmi. (2024). Music Recommendation System Using Alternating Least Squares Method. Indonesian Journal on Computing (Indo-JC), 9(1), 20–31. https://doi.org/10.34818/INDOJC.2024.9.1.908

Issue

Vol. 9 No. 1 (2024): April, 2024

Section

Computer Science

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