Paper Title
Recommendation system for song data using K-Means and K-Medoids Clustering algorithms
Article Identifiers
Authors
B.Anoohya , J Nikitha , Dr P Naga Jyothi , I Anish , G Bhargavi
Keywords
clustering, Recommendation, K-Means, K-medoids
Abstract
The ability to anticipate user preferences is crucial to recommendation systems. A personalized recommendation must take into account the listener's existing musical preferences as well as any changes to the "kind" of songs. This paper proposes a personalized next-song recommendation system. It utilizes Web API for Spotify to record the song features. K-means and K-medoids clustering algorithms are employed to identify similar songs using attributes. It is identified to which cluster the input music belongs. Content-based clustering is the term used for this. By computing the similarity measure, the songs that are "near" to the input song are identified next. Based on popularity metrics for this list of songs, the set of songs that should be played in order are identified.
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How To Cite (APA)
B.Anoohya, J Nikitha, Dr P Naga Jyothi, I Anish, & G Bhargavi (March-2023). Recommendation system for song data using K-Means and K-Medoids Clustering algorithms. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(3), e530-e533. https://ijnrd.org/papers/IJNRD2303467.pdf
Issue
Volume 8 Issue 3, March-2023
Pages : e530-e533
Other Publication Details
Paper Reg. ID: IJNRD_189881
Published Paper Id: IJNRD2303467
Downloads: 000121984
Research Area: Computer Science & TechnologyÂ
Country: Visakhapatnam, Andhra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2303467.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303467
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition


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