Abstract
Existing studies on music recommendation systems pose the problem of being incapable of proposing proper recommendations according to user conditions due to limited metadata obtained from users using a content-based filtering method. Although some studies have been conducted in recent years on recommendation systems employing a great amount of environmental information, they have been unable to satisfy information requested by the user. Thus, this study defines context information required to select music and proposes a hybrid filtering method that exploits a content-based filtering and collaborative filtering method in ubiquitous environments. In addition, this study developed a music recommendation system based on these filtering methods which significantly improved user satisfaction for music selection.
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Kim, JH., Jung, KY., Lee, JH. (2006). Hybrid Music Filtering for Recommendation Based Ubiquitous Computing Environment. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_82
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DOI: https://doi.org/10.1007/11908029_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47693-1
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