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FmFinder: Search and Filter Your Favorite Songs

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Advances in Visual Computing (ISVC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7431))

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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.

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Author information

Authors and Affiliations

  1. University of Illinois at Chicago, USA

    Tuan Nhon Dang, Anushka Anand & Leland Wilkinson

Authors
  1. Tuan Nhon Dang
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  2. Anushka Anand
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  3. Leland Wilkinson
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, 89512, Reno, NV, USA

    Darko Koracin

  5. Department of Computer Science, University of California at Irvine, 92697-3435, Irvine, CA

    Charless Fowlkes

  6. Eastman Kodak Company, 14650-2102, Rochester, NY, USA

    Sen Wang

  7. Department of Computer Science and Engineering, University of Colorado at Denver, 80217, Denver, CO, USA

    Min-Hyung Choi

  8. VRVis Zentrum für Virtual Reality and Visualisierung, 1220, Vienna, Austria

    Stephan Mantler

  9. California Institute for Telecommunications and Information Technology, University of California, 92093, San Diego, La Jolla, CA, USA

    Jürgen Schulze

  10. KAUST Visualizatioin Core Lab., 23955-6900, Thurwal, Saudi Arabia

    Daniel Acevedo

  11. Stony Brook University, 11794-4400, NY, USA

    Klaus Mueller

  12. Argonne National Laboratory, 60439, IL, USA

    Michael Papka

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© 2012 Springer-Verlag Berlin Heidelberg

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Dang, T.N., Anand, A., Wilkinson, L. (2012). FmFinder: Search and Filter Your Favorite Songs. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33179-4_34

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  • DOI: https://doi.org/10.1007/978-3-642-33179-4_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33178-7

  • Online ISBN: 978-3-642-33179-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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