Abstract
Choices in music express our taste and personality. Different people have different collections of favorite songs. The explosive growth of digital media makes it easier to access any songs we want. Consequently, finding the songs best fit to our tastes becomes more challenging. Existing solutions record user patterns of listening to music, then make recommendation lists for users. By applying information visualization techniques to this problem, we are able to provide users with a novel way to explore their list of recommendations. Based on that knowledge, users can filter the songs according to their needs and compare the music tastes of different groups of people. © 2012 Springer-Verlag.
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CITATION STYLE
Dang, T. N., Anand, A., & Wilkinson, L. (2012). FmFinder: Search and filter your favorite songs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7431 LNCS, pp. 348–358). https://doi.org/10.1007/978-3-642-33179-4_34
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