Cited By
View all- Amin SPhilips JTabrizi N(2019)Current Trends in Collaborative Filtering Recommendation SystemsServices – SERVICES 201910.1007/978-3-030-23381-5_4(46-60)Online publication date: 18-Jun-2019
Learning Listener's Preference for Music Recommender System
Pages 229 - 232
Abstract
Along with the spread of digital music and recent growth in the digital music industry, the demands for music recommender are increasing. These days, listeners have increasingly preferred to digital real-time streamlining and downloading to listen to music because this is convenient and affordable for the listeners. In this paper, we propose music recommender system using learning listener's prefererece, such as Melon, Billboard, Bugs Music, Soribada, and Gini, with most popular current songs across all genres and styles. It is also necessary for us to make the task of calculating the preference with weight to reflect the preference of most popular current songs with its popular music charts on trends. We evaluated the proposed system on the data set of music sites to measure its performance. We reported some of the experimental result, which is better performance than the previous system.
References
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Information & Contributors
Information
Published In
October 2015
321 pages
ISBN:9781450338462
DOI:10.1145/2837060
- Conference Chairs:
- Jongsup Choi,
- Sun Hwa Han,
- Joo-Yeoun Lee,
- Taeho Park,
- Editor:
- Aziz Nasridinov,
- Program Chairs:
- Carson K. Leung,
- Yoo-Sung Kim,
- Young-Koo Lee
Copyright © 2015 ACM.
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Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 October 2015
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- Short-paper
- Research
- Refereed limited
Conference
BigDAS '15
BigDAS '15: 2015 International Conference on Big Data Applications and Services
October 20 - 23, 2015
Jeju Island, Republic of Korea
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View all- Amin SPhilips JTabrizi N(2019)Current Trends in Collaborative Filtering Recommendation SystemsServices – SERVICES 201910.1007/978-3-030-23381-5_4(46-60)Online publication date: 18-Jun-2019
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