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Auflistung nach Autor:in "Goebl, Sebastian"

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  • Konferenzbeitrag
    Development and evaluation of a facebook-based product advisor for online dating sites
    (Datenbanksysteme für Business, Technologie und Web (BTW 2015) - Workshopband, 2015) Winter, Martin; Goebl, Sebastian; Hubig, Nina; Pleines, Christopher; Böhm, Christian
    Social networks play an important role in Web 2.0. For many users establishing contacts and staying in touch in the virtual world is more than just a spare time filler. In social networks like Facebook they provide much information about themselves in user profiles. Also for online dating the focus is on establishing new contacts. In general, three types of dating sites can be distinguished: more serious dating agencies, less focused singles' platforms, and casual dating sites. In the proposed paper we develop a product advisor that uses the Facebook profile information provided by a user to classify her or him to one of the three dating site categories which is most suitable for the user's purpose. For classification we use Naive Bayes. To train the classifier we investigate the correlation between profile information and choice of dating sites. We also evaluate this correlation on collected data of representative German dating sites. Although a sharp distinction is hard to find, tendencies and enlightening insights are revealed by the collected data.
  • Konferenzbeitrag
    Musical similarity analysis based on chroma features and text retrieval methods
    (Datenbanksysteme für Business, Technologie und Web (BTW 2015) - Workshopband, 2015) Englmeier, David; Hubig, Nina; Goebl, Sebastian; Böhm, Christian
    At the present day the world wide web is full of music. Highly effective algorithms for music compression and high data storage has made it easy to access all kind of music easily. However, it is not possible to look for a similar piece of music or a sound as easily as to google for a similar kind of text. Music is filtered by its title or artist. Although musicians can publish their compositions in a second, they will only be found by high youtube ratings or by market basket analysis. Less known artists need much luck to get heard, although their music might just be what people want to hear. To approach this issue, we propose a new framework called MIRA (Music Information Retrieval Application) for analyzing audio files with existing Information Retrieval (IR) methods. Text retrieval has already yielded many highly efficient and generally accepted methods to assess the semantic distance of different text. We use these methods by translating music into equivalent audio words based on chroma features. We show that our framework can easily match music interpreted even by different artists.