Hybrid Music Recommendation System Using Content-based Filtering and K-Mean Clustering Algorithm

With the advancement of technology there are millions of songs available on the internet and this creates problem for a person to choose from this vast pool of songs. So, there should be some middleman who must do this task on behalf of user and present most relevant songs that perfectly fits the user’s taste. This task is done by recommendation system. Music recommendation system predicts the user liking towards a particular song based on the listening history and profile. Most of the music recommendation system available today will give most recently played song or songs which have overall highest rating as suggestions to users but these suggestions are not personalized. The paper purposes how the recommendation systems can be used to give personalized suggestions to each and every user with the help of collaborative filtering which uses user similarity to give suggestions. The paper aims at implementing this idea and solving the cold start problem using content based filtering at the start.

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A music recommendation system with a dynamic k-means clustering algorithm

Sixth International Conference on Machine Learning and Applications (ICMLA 2007) ◽

10.1109/icmla.2007.97 ◽

2007 ◽

Cited By ~ 16

Author(s):

Dongmoon Kim ◽

Kun-su Kim ◽

Kyo-Hyun Park ◽

Jee-Hyong Lee ◽

Keon Myung Lee

Keyword(s):

Clustering Algorithm ◽

Recommendation System ◽

Music Recommendation ◽

Music Recommendation System

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Design and Implementation of Hybrid Music Recommendation System Based on Music Gene

Proceedings of the 2020 International Conference on Computers, Information Processing and Advanced Education ◽

10.1145/3419635.3419669 ◽

2020 ◽

Author(s):

Linfei Wang

Keyword(s):

Recommendation System ◽

Music Recommendation ◽

Design And Implementation ◽

Music Recommendation System

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Neural Collaborative For Music Recommendation System

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1071/1/012021 ◽

2021 ◽

Vol 1071 (1) ◽

pp. 012021

Author(s):

Abba Suganda Girsang ◽

Antoni Wibowo ◽

Jason ◽

Roslynlia

Keyword(s):

Recommendation System ◽

Music Recommendation ◽

Music Recommendation System

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Song Recommendations Based on Artists with Cosine Similarity Algorithms and K-Nearest Neighbor

JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) ◽

10.24843/jlk.2020.v08.i04.p01 ◽

2020 ◽

Vol 8 (4) ◽

pp. 367

Author(s):

Muhammad Arief Budiman ◽

Gst. Ayu Vida Mastrika Giri

Keyword(s):

Collaborative Filtering ◽

Mobile Phones ◽

Recommendation System ◽

Nearest Neighbor ◽

Cosine Similarity ◽

Music Recommendation ◽

K Nearest Neighbor ◽

Filtering Method ◽

K Nearest Neighbor Algorithm ◽

Music Recommendation System

The development of the music industry is currently growing rapidly, millions of music works continue to be issued by various music artists. As for the technologies also follows these developments, examples are mobile phones applications that have music subscription services, namely Spotify, Joox, GrooveShark, and others. Application-based services are increasingly in demand by users for streaming music, free or paid. In this paper, a music recommendation system is proposed, which the system itself can recommend songs based on the similarity of the artist that the user likes or has heard. This research uses Collaborative Filtering method with Cosine Similarity and K-Nearest Neighbor algorithm. From this research, a system that can recommend songs based on artists who are related to one another is generated.

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