Ubiquitous Music

Schiavoni, Flávio Luiz; Costalonga, Leandro

doi:10.4018/jcit.2015100102

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…In recent years, the music industry's financial model has shifted from being dependent on commodity sales to one that is centered on subscriptions and streaming. Digital music is now widely available compared to earlier times because of the new economic model in the music industry (Schiavoni and Costalonga, 2015). As a result, the importance of the music recommender system for music suppliers cannot be overstated.…”

Section: Introductionmentioning

confidence: 99%

Folk Music Recommendation Using NSGA-II Optimization Algorithm

Sarkar,

Rai,

Kumar

et al. 2023

Journal of Computer Science

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Music recommendation systems can significantly improve the listening and search experiences of a music library or music application. There is simply too much music on the market for a user to navigate tens of millions of songs effectively. Because of the high demand for excellent music recommendations, the field of Music Recommendation Systems (MRS) is rapidly expanding. The main motivation for developing the rating-based recommendation system was to extract relevant information from user reviews of instrumental music. In this study, we suggest an NSGA-II-based music recommendation system based on user interest, popularity of an instrument, and total cost. Our aim is to maximize user interest and popularity while minimizing the costs. We also compared our method to the baseline algorithm and discovered that it outperforms the baseline approaches. We used real-world metrics like precession, recall, and F1-score to compare our method to the baseline approaches.

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Section: Introductionmentioning

confidence: 99%