IEEE Style Citation: Priyank Jain , Vamsikrishna Patchava, “Music Recommendation Based on Subjective Attributes,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.80-91, 2015.
MLA Citation
MLA Style Citation: Priyank Jain , Vamsikrishna Patchava "Music Recommendation Based on Subjective Attributes." International Journal of Computer Sciences and Engineering 3.10 (2015): 80-91.
APA Citation
APA Style Citation: Priyank Jain , Vamsikrishna Patchava, (2015). Music Recommendation Based on Subjective Attributes. International Journal of Computer Sciences and Engineering, 3(10), 80-91.
BibTex Citation
BibTex Style Citation:
@article{Jain_2015,
author = {Priyank Jain , Vamsikrishna Patchava},
title = {Music Recommendation Based on Subjective Attributes},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2015},
volume = {3},
Issue = {10},
month = {10},
year = {2015},
issn = {2347-2693},
pages = {80-91},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=711},
publisher = {IJCSE, Indore, INDIA},
}
RIS Citation
RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=711
TI - Music Recommendation Based on Subjective Attributes
T2 - International Journal of Computer Sciences and Engineering
AU - Priyank Jain , Vamsikrishna Patchava
PY - 2015
DA - 2015/10/31
PB - IJCSE, Indore, INDIA
SP - 80-91
IS - 10
VL - 3
SN - 2347-2693
ER -
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Abstract
Majority of the music recommendation systems in use today use historical preferences of other users having similar taste to recommend songs to a particular user. Other systems use past preferences of the current user and musical attributes of songs to make recommendations. In this paper, we use a novel approach to recommend music to users based on content-based filtering. This system can be used both as a search engine and for making recommendations. Moreover, this system does not suffer from the cold start problem which most of the recommender systems suffer from. Our system has a very small learning curve. We present a simple yet fast approach to make music recommendations using echonest’s music attributes. The system is based on calculating the Euclidean distance to find out top recommended songs. This system can be used in combination with traditional recommendation systems for more effective recommendation. We think music users will find this system easy to use and experiment with and therefore helpful to discover new music. This system will result in increased enjoyment of music for users.
[1] Spotify, music for everyone. https://www.spotify.com, Aug 2015
[2] Collaborative Filtering at Spotify, slide 4 of 63. http://www.slideshare.net/erikbern/collaborative-filtering-at-spotify-16182818, Aug 2015
[3] Pandora Internet Radio, listen to free music you’ll love. http://www.pandora.com, Aug 2015
[4] Michael Howe. “Pandora’s Music Recommender”. http://courses.cs.washington.edu/courses/csep521/07wi/prj/michael.pdf
[5] Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere. The Million Song Dataset. In Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), 2011.
[6] Echonest. http://developer.echonest.com/docs/v4, Sep 2015
[7] Acoustic Attributes, The Echo Nest Developer Center. http://developer.echonest.com/acoustic-attributes.html, Sep 2015
[8] Danceability and Energy: Introducing Echo Nest. http://runningwithdata.com/post/1321504427/danceability-and-energy, Sep 2015
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