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Complicated TV Made Easy, Again

Personalizing Video Entertainment in the Age of Abundance [of content, technology and business models].
Presented by ContentWise teams at ACM Conference on Recommender Systems held in Vienna, Austria - September 2015

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Complicated TV Made Easy, Again

  1. 1. Complicated TV made easy, again Personalizing video entertainment
 in the age of abundance
 (of content, technology and business models) ACM RecSys Conference Vienna 2015
  2. 2. ContentWise learns user’s taste and habits and surfaces relevant content The ContentWise software suite blends the power of recommendation algorithms with the convenience of • editorial and operational tools • assisted content curation • business rules • analytics • a/b testing • metadata enhancement
  3. 3. Complicated TV made easy, again
  4. 4. History of TV (super-simplified) Early Broadcasting Thematic Channels Early On-demand
 DVR & Home Video Internet Streaming Unilateral programming curation Abundance of channels Narrow audience segments “TV on my terms” Program the DVR Rent/buy DVDs Overwhelming offer Aggregation of dispersed audiences Long-tail effects Hard to find relevant content Too many channels for surfing Where’s the TV Guide?
  5. 5. Content looks like a moving target Live Airing Restart TV Lookback EPG Video Recording Catchup (free for X days) SVOD Catalog (older stuff?) TVOD
 ($$$) OTT Apps (scattered content) ?! Where is the next episode?
  6. 6. GOAL Predict the next user’s action and user’s intent
  7. 7. WHY? Reduce TIME-TO-CHOICE Content offering
 is huge Screens
 are small Attention span
 is short Did you say “scroll”?Did you say “search”? I’m sorry, what did you say? (otherwise you lose the user)
  8. 8. Traditional “taste profile” has limitations No adaptation to user’s lifestyle No adaptation to context No user’s intent Taste Profile Very valuable, sophisticated; but almost static Types Topics Actors Genres Directors Storytelling …
  9. 9. User’s lifestyle Habits Consumption patterns User’s intent, in session
  10. 10. Let’s consider additional profiling dimensions DEVICE TYPE TIME LOCATION WEATHER? NEWS? HOLIDAYS? …
  11. 11. Unified Profiling Linear TV On-demand Profile Find a way to create a unified profile from heterogeneous interaction schemes and data models
  12. 12. Prediction Discovery Surfacing the next available episode for each series the user is already following Surfacing Next Episodes + Suggesting New TV Series Suggesting pilots from series the user may like to try or promoted series
  13. 13. Surface what’s relevant, for me, on this device, here, now
  14. 14. Danke schön! pan@contentwise.tvYes, we’re hiring!