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Enhancing Metadata Generation through Expert Identification in Games with a Purpose

This study explores how games with a purpose can generate valuable data by distinguishing between expert and non-expert players. We hypothesize that expert players can significantly enhance metadata discovery in music information retrieval. By validating generic music metadata and creating complex song relationships that aren't easily discovered through standard tagging, we aim to utilize player expertise for faster and more accurate results. Our methodology includes local library scanning, analysis of listening history, and engaging players through quiz mini-games.

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Enhancing Metadata Generation through Expert Identification in Games with a Purpose

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Presentation Transcript

  1. Expert Finding and Metadata Generation with Games with a Purpose Peter Dulačka Jakub Šimko

  2. Problem • Gameswith a purpose are generatingdata • Expert data and wrongdatacannot be distinguished and they are being treatedthe sameway (trashedand ignored) • Hypothesis:wecandistinguishexperts in gameswith a purpose and utilizethemfor more accurate / fastermetadatadiscovery

  3. Game and Purpose • Validation/creation of generic music metadata (non-expert and expert players) • Creation of non-trivial song relationships not easily discoverable by tag similarity (expert players only)

  4. Expert Finding Solution • MusicInformationRetrieval • Internet radio(custom or existing) • Discoveringplayer’s expertise in specificmusic domain(primary objective) • Means • Locallibraryscanning / listeninghistory (LastFM) • Factquestioning - quizminigames • Crowdsouring

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