Your SlideShare is downloading. ×
0
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Multimedia  Personalization
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Multimedia Personalization

1,742

Published on

Trends in multimedia personalization and recommendation engines

Trends in multimedia personalization and recommendation engines

Published in: Business, Technology
0 Comments
4 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
1,742
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
79
Comments
0
Likes
4
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1.  
  • 2.
    • 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
  • 3.
    • 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
  • 4.
    • 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
  • 5.  
  • 6. Source: TrustedOpinion Technology is mostly: natural language processing Correlation matrix
  • 7.  
  • 8.  
  • 9.
    • Analyze the user taste in order to provide the best personalized music channel
    • iLike music recommendation app growth on facebook
  • 10.
    • Commercial version of MovieLens with better features & GUI
    Collaborative /Peer based Content based
  • 11.
    • Recommendation/Rating:
      • Based on the known 1-5 star system
      • Use of peer/group recommendation icon
  • 12.
    • Rating is kept simple:
      • Like it/hate it/no opinion
    • Recommend ions are:
      • You’ll love it
      • You might love it
  • 13.
    • 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
  • 14. 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
  • 15.
    • Recommendation Systems closes the loop between content creator/distributers and the users
  • 16.  
  • 17.
    • 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
  • 18.
    • Adobe Flash and Microsoft Silverlight Enables:
      • Using one video version
      • Changing the video advertisement per user without transcoding the video
  • 19.
    • Pre and Post Roll
    • Overly
    Doomsday Movie advertisement as Overlay on a pre-roll of Doomsday video
  • 20.  
  • 21.  

×