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Association Rule Mining and Audio Signal Processing for Music Discovery and Recommendation

Association Rule Mining and Audio Signal Processing for Music Discovery and Recommendation

Md. Mahfuzur Rahman Siddiquee, Md. Saifur Rahman, Shahnewaz Ul Islam Chowdhury, Rashedur M. Rahman
Copyright: © 2016 |Volume: 4 |Issue: 2 |Pages: 17
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781466693838|DOI: 10.4018/IJSI.2016040105
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MLA

Siddiquee, Md. Mahfuzur Rahman, et al. "Association Rule Mining and Audio Signal Processing for Music Discovery and Recommendation." IJSI vol.4, no.2 2016: pp.71-87. http://doi.org/10.4018/IJSI.2016040105

APA

Siddiquee, M. M., Rahman, M. S., Chowdhury, S. U., & Rahman, R. M. (2016). Association Rule Mining and Audio Signal Processing for Music Discovery and Recommendation. International Journal of Software Innovation (IJSI), 4(2), 71-87. http://doi.org/10.4018/IJSI.2016040105

Chicago

Siddiquee, Md. Mahfuzur Rahman, et al. "Association Rule Mining and Audio Signal Processing for Music Discovery and Recommendation," International Journal of Software Innovation (IJSI) 4, no.2: 71-87. http://doi.org/10.4018/IJSI.2016040105

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Abstract

In this research, the authors propose an intelligent system that can recommend songs to user according to his choice. They predict the next song a user might prefer to listen based on their previous listening patterns, currently played songs and similar music based on music data. To calculate music similarity the authors used a Matlab toolbox that considers audio signals. They used association rule mining to find users' listening patterns and predict the next song the user might prefer. As they propose a music discovery service as well, the authors use the information of music listening pattern and music data similarity to recommend a new song. Later in result section, they replaced the audio based similarity with last.fm api for similar song listing and analyzed the behaviour of their system with the new list of songs.

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