Wieviel Musik- empfehlungen braucht der Mensch | Stephan Baumann (DFKI)

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    Wieviel Musik- empfehlungen braucht der Mensch | Stephan Baumann (DFKI) - Presentation Transcript

    1. yet another SocialFacebookiPh oneAndroidTwitte rHotMusicSharing StreamingApp ??? HORST 17.09.2009
    2. Nein. HORST 17.09.2009
    3. HOlistic Recommender & Storytelling Technology
    4. HORST = 1 Option ? HORST 17.09.2009
    5. Die sogenannten User sind ja auch Menschen. Vielfalt, Emotion. HORST 17.09.2009
    6. HORST 17.09.2009
    7. HORST 17.09.2009
    8. HORST 17.09.2009
    9. HORST 17.09.2009
    10. HORST 17.09.2009
    11. TYP Musiker, Musikpoweruser, Konzertgänger, CD-Käufer Computerforscher (DFKI), Recommender Vorlesung (Pop Akademie), Social Media Projektleiter (DFKI Berlin), Gründer MUSIC INTEREST Electronica, Funk, Soul, Hamburger Schule, Indietronics, Glitch, Minimal, Jazz, NuTango MOOD Context-related: Concentrated <> Ecstatic HORST 17.09.2009
    12. HORST 17.09.2009
    13. Technologie ist toll. Manchmal ein wenig kühl. Vielfalt, Präzision. HORST 17.09.2009
    14. Empfehlungsmaschinen funktionieren prinzipiell ... 3 klassische Ansätze: - Collaborative Filtering (CF) - Content-Based Filtering (CBF) - Hybrid: Kombinationen von CF+CBF Neuere Ansätze - Social: Blip,Hypemachine,TwitterDJ,Mashups - Semantic: DBTune HORST 17.09.2009
    15. Collaborative Filtering: Implizit/Explizit per User Stephan Martin Dominik! Song Nr.1 60mal / Tag - 99mal / 2005! Song Nr.2 3 mal / Woche 4mal / 2005 -! Song Nr.3 1 mal / 2006 20mal / Woche -! HORST 17.09.2009
    16. Content-based Filtering: Manuelle Erfassung Tonart Taktart Geschlecht! Song Nr.1 D Moll 4/4 weiblich! Song Nr.2 D Dur 3/4 männlich! Song Nr.3 A Dur 4/4 -! HORST 17.09.2009
    17. Content-based Filtering: Maschinelle Erfassung Klangverlauf WWW-Infos Liedtext! Song Nr.1 funky, Duo „..so lovely..“! Song Nr.2 traditionell „ .. my Love“! Song Nr.3 instrumental „“! HORST 17.09.2009
    18. Social: Blogs, Microblogs, Mashups, ... HORST 17.09.2009
    19. Social: Blogs, Microblogs, Mashups, ... HORST 17.09.2009
    20. Social: Blogs, Microblogs, Mashups(G.Jost), ... !"#$%%$&'()*&++,,,-./.$01234%#%%-5/.+6778+76+%29:.;(4*$;.25(<0$;=%<*>.; =$91;.#9<5;9$3?<5$+@' HORST 17.09.2009
    21. Social: Blogs, Microblogs, Mashups(J.Herkowitz), ... !"#$%%$&'()&**+++,-%./0%%1234,4.5*6778*79*%0:$2:;$<)$=15$>:;/%1)?5; =$4.55$>@$=,':5%AB HORST 17.09.2009
    22. Empfehlungsmaschinen funktionieren ... gut genug? [MagnoSable ISMIR2008] A Comparison of Signal-based Music Recommendation to Genre Labels, Collaborative Filtering, Musicological Analysis, Human Recommendation, and Random Baseline !!! 15 Probanden !!! YES HORST 17.09.2009
    23. Empfehlungsmaschinen funktionieren ... gut genug? [PachetRoy ISMIR2008] Hit Song Science is not yet a Science !!! 32.000 Songs, HIFIND Experten Daten!!! NO HORST 17.09.2009
    24. Empfehlungsmaschinen funktionieren ... gut genug? [GodfreyChordia ISMIR2008] Hubs and Homogenity: Improving Content-Based Music Modeling !!! 617 Songs, OpenNap Experten Daten!!! ? HORST 17.09.2009
    25. Empfehlungsmaschinen funktionieren ... gut genug? [Salganik et al SCIENCE2006] Experimental Study of Inequality and Unpredictability in an Artifical Cultural Market !!! 14341 Probanden !!! NO HORST 17.09.2009
    26. Empfehlungsmaschinen funktionieren ... gut genug? [FlederHosanagar ManagementScience2009] Blockbuster Culture‘s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity !!! Mathemat.Simulation !!! ? HORST 17.09.2009
    27. Empfehlungsmaschinen funktionieren ... gut genug? [BergerLeMens PNAS2009] How Adoption Speed affects the Abandonment of Cultural Tastes !!! Verwendung statistischer externer Daten !!! „adopt quickly=die fast“ HORST 17.09.2009
    28. Empfehlungsmaschinen funktionieren ... gut genug? [Celma PH.D THESIS2009] Music Recommendation and Discovery in the Long Tail !!! 288 Probanden !!! !!! 3 verschiedene Empfehlungssysteme!!! CF even better than HYBRID HORST 17.09.2009
    29. Semantik und Storytelling, was soll das jetzt? HORST 17.09.2009
    30. Musikalische Fakten sind digital verfügbar HORST 17.09.2009
    31. Was ist damit möglich? Konzepte Virgin Suicides Alltogether Miner‘s Son Sofia Coppola Beth Hirsch Rhodes J.B. Dunkel Thomas Mars Lost in Translation Etienne de Crecy PHOENIX Too Young AIR ORANGE Pharell Williams Angel Eyewater CASSIUS Feeling for you Gwen McRae Philippe Zdar All this love that I‘m giving HORST 17.09.2009
    32. Was ist damit möglich? Beziehungen directed composed contains composed played married friends directed member lead singer tour manager contains soundtrack composed friends 2006 charts 65 related performed member composed worked with composed performed member contains a sample HORST 17.09.2009
    33. Was ist damit möglich? Jeder Pfad eine Story! Virgin Suicides Alltogether Miner‘s Son composed contains directed Sofia Coppola composed Beth Hirsch Rhodes married friends played directed J.B. Dunkel Thomas Mars Lost in Translation member lead singer Etienne de Crecy PHOENIX Too Young contains soundtrack tour manager composed AIR friends 2006 charts 65 ORANGE related Pharell Williams Angel member composed Eyewater worked with performed CASSIUS Feeling for you composed performed member contains a sample Gwen McRae Philippe Zdar All this love that I‘m giving HORST 17.09.2009
    34. Jeder Pfad eine Story! Persönliche Aspekte Virgin Suicides Alltogether Miner‘s Son composed contains directed Sofia Coppola composed Beth Hirsch Rhodes married friends played directed J.B. Dunkel Thomas Mars Lost in Translation member lead singer Etienne de Crecy PHOENIX Too Young contains soundtrack tour manager composed AIR friends 2006 charts 65 ORANGE related Pharell Williams Angel member composed Eyewater worked with performed CASSIUS Feeling for you composed performed member contains a sample Gwen McRae Philippe Zdar All this love that I‘m giving HORST 17.09.2009
    35. Interessante Stories von Maschinen? ja, das ist hart, ergo forschungsreleva nt HORST 17.09.2009
    36. „Fresh Brainfood“ Algorithm Design [Wilcock 200x] Talking OWLs: Towards an Ontology Verbalizer [Galanis et al 2009] An Open-Source Natural Language Generator for OWL Ontologies and its Use in Protégé and Second Life [Górka et al 2007] Information System Based on Natural Language Generation from Ontology HORST 17.09.2009
    37. Budget? Sie machen wohl Witze. 100% VC- free, kein direktes Research Funding HORST 17.09.2009
    38. GRAFIX HORST 17.09.2009
    39. Co-Work: Co-Create, Co- Design, Co-Research, Co-X Visual & Brand Design: 2erPack Research & Experience Design: DFKI,... CBF+CF-Product Support: BMAT(?),DFKI-Openeer,... Social Media Monitoring: tiqqer(?),... MusicBiz Feedback: Sony,Aupeo,MotorFM,Musicload SocioCulturalMusicManagement Input+Students: Pop Akademie SMIXLab, Universität Potsdam,... HORST 17.09.2009
    40. ... und die sogenannten User abholen ... Vielfalt, Emotion! HORST 17.09.2009
    41. ... wenigstens viral? Identifikationspot enzial scheint vorhanden HORST 17.09.2009
    42. lovehorst.com
    SlideShare Zeitgeist 2009

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