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Multimedia  Personalization

Multimedia Personalization



Trends in multimedia personalization and recommendation engines

Trends in multimedia personalization and recommendation engines



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    Multimedia  Personalization Multimedia Personalization Presentation Transcript

      • Customization and personalization aspects:
        • Place (home, mobile)
        • Time
        • Content
      • Personalization/Recommendation engines
        • Intro to media recommendation methods
        • Media personalization in IPTV, Mobile and Web
      • Personalization in advertisement - Targeting
        • Personalized video Ads on mobile & Web
        • Seambi example
      • Place
      • Time
      • Content type
      • This lecture concentrate on how to adapt/personalize the content.
      Place adaptation Content Type adaptation Time adaptation Personalized content Broadcast TV VOD PVR Owned content UGC PC Mobile
      • There is a surge in the amount of available content:
        • 1 channel -> Thousands of channels
        • 10 VoD Titles->Thousands of VoD titles
        • Tens of millions of UGC clips
      • How to select the right content?
        • Let the system select the content for you
      • Media Recommendation Engines
    • Source: TrustedOpinion Technology is mostly: natural language processing Correlation matrix
      • Analyze the user taste in order to provide the best personalized music channel
      • iLike music recommendation app growth on facebook
      • Commercial version of MovieLens with better features & GUI
      Collaborative /Peer based Content based
      • Recommendation/Rating:
        • Based on the known 1-5 star system
        • Use of peer/group recommendation icon
      • Rating is kept simple:
        • Like it/hate it/no opinion
      • Recommend ions are:
        • You’ll love it
        • You might love it
      • Once we are sure of users needs we can assist him in creating his own “Personal TV Channel”
      • Personal channel is most common in:
        • Music Recommendation Engine
        • Mobile recommendation engine
      • Starting to catch up on Web TV channels
      • Channel Creation Platforms
    • Future Present Past Process My Customized channel UGC / Pre defined channels Broadcast Pre defined channels Distribution Lean back Lean Forward Text search of clips Lean Back – open loop Consumption UGC + Legal Premium content+Meshups UGC content + Stolen Premium content Premium Content Content type
      • Recommendation Systems closes the loop between content creator/distributers and the users
      • In broadcast TV ->broadcast Ads
      • In IPTV and Web we can use flash->video advertisement without transcoding.
      • Use targeting and personalization engine
      • Advertisers use viewers information for ad targeting including:
        • Location
        • Demographic
        • User profile
        • User content
      • Adobe Flash and Microsoft Silverlight Enables:
        • Using one video version
        • Changing the video advertisement per user without transcoding the video
      • Pre and Post Roll
      • Overly
      Doomsday Movie advertisement as Overlay on a pre-roll of Doomsday video