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Title:
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FINDING SIMILAR MUSIC ARTISTS FOR RECOMMENDATION |
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Author(s):
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Lisa Wiyartanti , Laehyun Kim |
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ISBN:
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978-972-8924-93-5 |
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Editors:
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Pedro Isaías, Bebo White and Miguel Baptista Nunes |
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Year:
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2009 |
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Edition:
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1 |
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Keywords:
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Information retrieval, music contents, artist similarity; user rating |
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Type:
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Full Paper |
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First Page:
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535 |
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Last Page:
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542 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Music information retrieval had become an interesting research subject to be explored. The development of information
clustering leads the user to find related contents and interests more easily. In this paper, we present a recommendation of
similar music artists based on the music genre classification, artists era, and social rating information. The algorithm is
performed in three steps: compute similarity measure on music genre; apply the user rating factor to the artist; and
finalize the similarity by selecting artists who have the same period of music activities. The Jaccard coefficient and
Nearest-Neighbor search have been used in the computation. The experiment shows that we can obtain better results
using the proposed method. |
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