TVOT June 2012

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  • metadata and responsiveness to different business models, yields almost unimaginable reservoir from which substantial business value can be derived.As a start, an operator can review different ways to reach for content and how these ways can be optimized towards different audiences. For example, in Orca’s Content Discovery system - COMPASS – 4 main approaches are provided to discover content, recommendations, search, social TV and exploration. By utilizing these approaches, it’s now easier to understand how your audience find and consume content. Furthermore, these discovery methods are further supported by different discovery engines: Collaborative Filtering, Semantic Recommendations, Users’ Profiles, Social TV, Operator Promotions, Popularity Based and External Critics, which provide insights into which engine serves which audience.
  • metadata and responsiveness to different business models, yields almost unimaginable reservoir from which substantial business value can be derived.As a start, an operator can review different ways to reach for content and how these ways can be optimized towards different audiences. For example, in Orca’s Content Discovery system - COMPASS – 4 main approaches are provided to discover content, recommendations, search, social TV and exploration. By utilizing these approaches, it’s now easier to understand how your audience find and consume content. Furthermore, these discovery methods are further supported by different discovery engines: Collaborative Filtering, Semantic Recommendations, Users’ Profiles, Social TV, Operator Promotions, Popularity Based and External Critics, which provide insights into which engine serves which audience.
  • Research done with Prof. J. Goldenberg (Hebrew University- Jerusalem) and Dr. G Ostreicher and S Reichman from Tel Aviv university
  • Research done with Prof. J. Goldenberg (Hebrew University- Jerusalem) and Dr. G Ostreicher and S Reichman from Tel Aviv university
  • Research done with Prof. J. Goldenberg (Hebrew University- Jerusalem) and Dr. G Ostreicher and S Reichman from Tel Aviv university
  • Research done with Prof. J. Goldenberg (Hebrew University- Jerusalem) and Dr. G Ostreicher and S Reichman from Tel Aviv university
  • Research done with Prof. J. Goldenberg (Hebrew University- Jerusalem) and Dr. G Ostreicher and S Reichman from Tel Aviv university
  • Research done with Prof. J. Goldenberg (Hebrew University- Jerusalem) and Dr. G Ostreicher and S Reichman from Tel Aviv university
  • Research done with Prof. J. Goldenberg (Hebrew University- Jerusalem) and Dr. G Ostreicher and S Reichman from Tel Aviv university
  • TVOT June 2012

    1. 1. 8 lessons from deploying ContentDiscovery solution at Orange (France)Dr. Ofer WeintraubVP Innovation – Viaccess-Orca
    2. 2. Who we are ?Viaccess-Orca
    3. 3. About ProductsFounded – June 13 2012 CAS / DRMFully owned by FranceTelecom Personalized Content Discovery platform that350 employees recommendsOffices: France, US, Hong- the right contentKong, Israel100+ customers worldwide TV Everywhere Flexible middleware platform delivering a full array of IPTV & OTT services – including live TV, VOD and PVR, across multiple screens
    4. 4. Quick primerContent Discovery
    5. 5. 3 main ways to discover content Search Recommendations Exploration I know what The service knows I’d like to explore I am looking for what I am looking for with my personal guide
    6. 6. 3 main ways to discover content Search Recommendations ExplorationAuto-complete Collaborative Personal zone filteringAuto-suggest Trends NLP - semanticSocial-aided Games SocialPersonalized Gossip Popular/Trending search Deals Lists (experts, Smart filters operator, users) Friends
    7. 7. Search example
    8. 8. Recommendations example
    9. 9. Explore example
    10. 10. How it generally Works ? Filtering Engines Advanced semantic Usage Discovery data Collaborative Manager filteringEvent Registration User Profiles Middleware (CMS, CRM)
    11. 11. Content Discovery for every service….VOD Linear TV series
    12. 12. … and on any device
    13. 13. measuring the value ofContent Discovery system
    14. 14. 75% of what people watch is from some sort of recommendation. Netflix blog April 2012 35% of Amazon sales are due to recommendations Venturebeat - 2006Nice numbers but no single way to measure success…
    15. 15. Typical measures Novelty Accuracy Satisfaction Coverage Serendipity
    16. 16. Testing methods Train Predict Subjective testing
    17. 17. Lessons learned
    18. 18. Lesson 1 –have a dedicated group of real users for tests 15% 15% satisfaction gain in 1 week by adding bots and tuning thresholds and filtering in collaborative-filtering
    19. 19. Lesson 2 –avoiding the rottenapple is more importantthan getting the perfectonesWe’ve seen dramatic jumps insatisfaction when pruning badresults- Time slices- Thresholds- Exclude rules- Adequate “system warming”- External guides (e.g. popularity)
    20. 20. Lesson 3 –Multiple engines (i.e. engines blend ) help overcoming thefilter- bubble effect Overall satisfaction Rating Semantic (1 – 10) ( 1- 5) Single Network 6.01 2.72 Dual Social network 8.00 3.14 Change 33% ~15% Research: The Effect of Dual Networks Prof. J. Goldenberg, Dr. G Ostreicher and S Reichman - Feb. 2011
    21. 21. Lesson 4 –Collaborative filtering is popularity biased,users prefer noveltyThe highest correlation found in early experiments isbetween novelty and purchase decision (0.55)Which one to recommend ifscore is the same ?Avatar ?The Pianist ? Research: Negotiation agents n=50 x 2 (US, India) Prof. S. Kraus and Dr. A. Hasidim - Apr. 2012 (not yet published)
    22. 22. Lesson 5 – OMG it speaks French Fear Peurabhorrence, agitation, angst, anxiety, aversion,awe, chickenheartedness, cold feet,cold sweat, concern, consternation, cowardice,creeps, despair, discomposure, dismay,disquiet panique, phobie, frayeur, appréhension, frisson,ude, distress, doubt, dread,faintheartednes épouvante, crainte, alarme, émotion, affolements, foreboding, fright, funk, horror,jitters, misgiving, nightmare, panic, phobia,presentiment, qualm, recreancy, reverence,revulsion, scare, suspicion, terror, timidity,trembling, tremor, trepidation, unease, uneasiness, worry Change in words statistics, change of sources , change in amount of reviews, change of vocabulary, correlation to English data is not always clear
    23. 23. Lesson 6 – Cold start could get really cold…. System bootstrap User cold start Content cold start Add users Non-personal Non-personal Add values Implicit evidence Implicit evidence Hybrid methods Questionnaire Aggregated data
    24. 24. Lesson 7 – Laziness wins….
    25. 25. Lesson 8 – Privacy matters Explicit / Implicit - Channels - GenresProfile visibility - Actors - Devices - Subscriptions - Black / White lists Still relevant - Semantic without history Enough value - Popularitywhen not opted-in - Trends - Lists - General CF
    26. 26. Other points to consider Provide reason Recommended because 3 of your friends liked it Channel zappingVOD order Program recordingVOD content end Setting a reminderVOD rating Launches ofAdd to wish list COMPASS Collect valuable indirect evidenceShow movie Explicit inputpreview Exclude contentChannel zapping Search terms Morning Evening Noon Night Handle “time” with care
    27. 27. It’s an on going processIt’s getting better every monthIt’s a lot of fun
    28. 28. Anyone?

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