User Playlist-Based Music Recommendation Using Music Metadata Embedding 


Vol. 13,  No. 8, pp. 367-373, Aug.  2024
https://doi.org/10.3745/TKIPS.2024.13.8.367


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  Abstract

The growth of mobile devices and network infrastructure has brought significant changes to the music industry. Online streaming services has allowed music consumption without constraints of time and space, leading to increased consumer engagement in music creation and sharing activities, resulting in a vast accumulation of music data. In this study, we define metadata as “song sentences” by using a user's playlist. To calculate similarity, we embedded them into a high-dimensional vector space using skip-gram with negative sampling algorithm. Performance eva luation results indicated that the recommended music algorithm, utilizing singers, genres, composers, lyricists, arrangers, eras, seasons, emotions, and tag lists, exhibited the highest performance. Unlike conventional recommendation methods based on users' behavioral data, our approach relies on the inherent information of the tracks themselves, potentially addressing the cold start problem and minimizing filter bubble phenomena, thus providing a more convenient music listening experience.

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[IEEE Style]

K. M. Nam, Y. R. Park, J. Y. Jung, D. H. Kim, H. H. Kim, "User Playlist-Based Music Recommendation Using Music Metadata Embedding," The Transactions of the Korea Information Processing Society, vol. 13, no. 8, pp. 367-373, 2024. DOI: https://doi.org/10.3745/TKIPS.2024.13.8.367.

[ACM Style]

Kyoung Min Nam, Yu Rim Park, Ji Young Jung, Do Hyun Kim, and Hyon Hee Kim. 2024. User Playlist-Based Music Recommendation Using Music Metadata Embedding. The Transactions of the Korea Information Processing Society, 13, 8, (2024), 367-373. DOI: https://doi.org/10.3745/TKIPS.2024.13.8.367.

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