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Similaire à TVOT June 2012
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TVOT June 2012
- 1. 8 lessons from deploying Content Discovery solution at Orange (France) Dr. Ofer Weintraub VP Innovation – Viaccess-Orca
- 2. Who we are ? Viaccess-Orca
- 3. About Products Founded – June 13 2012 CAS / DRM Fully owned by France Telecom Personalized Content Discovery platform that 350 employees recommends Offices: France, US, Hong- the right content Kong, Israel 100+ customers worldwide TV Everywhere Flexible middleware platform delivering a full array of IPTV & OTT services – including live TV, VOD and PVR, across multiple screens
- 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. 3 main ways to discover content Search Recommendations Exploration Auto-complete Collaborative Personal zone filtering Auto-suggest Trends NLP - semantic Social-aided Games Social Personalized Gossip Popular/Trending search Deals Lists (experts, Smart filters operator, users) Friends
- 10. How it generally Works ? Filtering Engines Advanced semantic Usage Discovery data Collaborative Manager filtering Event Registration User Profiles Middleware (CMS, CRM)
- 11. Content Discovery for every service…. VOD Linear TV series
- 12. … and on any device
- 13. measuring the value of Content Discovery system
- 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 - 2006 Nice numbers but no single way to measure success…
- 15. Typical measures Novelty Accuracy Satisfaction Coverage Serendipity
- 16. Testing methods Train Predict Subjective testing
- 17. Lessons learned
- 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. Lesson 2 – avoiding the rotten apple is more important than getting the perfect ones We’ve seen dramatic jumps in satisfaction when pruning bad results - Time slices - Thresholds - Exclude rules - Adequate “system warming” - External guides (e.g. popularity)
- 20. Lesson 3 – Multiple engines (i.e. engines blend ) help overcoming the filter- 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. Lesson 4 – Collaborative filtering is popularity biased, users prefer novelty The highest correlation found in early experiments is between novelty and purchase decision (0.55) Which one to recommend if score 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. Lesson 5 – OMG it speaks French Fear Peur abhorrence, agitation, angst, anxiety, aversi on, awe, chickenheartedness, cold feet,cold sw eat, concern, consternation, cowardice,cree ps, despair, discomposure, dismay,disquiet panique, phobie, frayeur, appréhension, frisson, ude, distress, doubt, dread,faintheartednes épouvante, crainte, alarme, émotion, affolement s, foreboding, fright, funk, horror,jitters, mis giving, nightmare, panic, phobia,presentime nt, qualm, recreancy, reverence,revulsion, s care, suspicion, terror, timidity,trembling, tre mor, 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. 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. Lesson 7 – Laziness wins….
- 25. Lesson 8 – Privacy matters Explicit / Implicit - Channels - Genres Profile visibility - Actors - Devices - Subscriptions - Black / White lists Still relevant - Semantic without history Enough value - Popularity when not opted-in - Trends - Lists - General CF
- 26. Other points to consider Provide reason Recommended because 3 of your friends liked it Channel zapping VOD order Program recording VOD content end Setting a reminder VOD rating Launches of Add to wish list COMPASS Collect valuable indirect evidence Show movie Explicit input preview Exclude content Channel zapping Search terms Morning Evening Noon Night Handle “time” with care
- 27. It’s an on going process It’s getting better every month It’s a lot of fun
- 28. Anyone?
Notes de l'éditeur
- 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