Recommendation system for song data using K-Means and K-Medoids Clustering algorithms
B.Anoohya
, J Nikitha , Dr P Naga Jyothi , I Anish , G Bhargavi
clustering, Recommendation, K-Means, K-medoids
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.
"Recommendation system for song data using K-Means and K-Medoids Clustering algorithms", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 3, page no.e530-e533, March-2023, Available :https://ijnrd.org/papers/IJNRD2303467.pdf
Volume 8
Issue 3,
March-2023
Pages : e530-e533
Paper Reg. ID: IJNRD_189881
Published Paper Id: IJNRD2303467
Downloads: 000118885
Research Area: Computer Science & Technology
Country: Visakhapatnam, Andhra Pradesh, India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
