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Metadata for Online TV & Video - by ContentWise

What are the challenges of handling complex metadata environments? How to handle the complexity of multiple metadata sources, eliminate data gaps and duplicates, how to avoid re-tagging catalog items and how to assist editors in data curation. From the ContentWise team.

Metadata for Online TV & Video - by ContentWise

  1. 1. The Personalization Toolkit Metadata Essentials Web Session, September 2015 Pancrazio Auteri, CTO Kauser Kanji, Editor
  2. 2. Well-managed metadata is critical
  3. 3. Metadata: a lot of manual work
  4. 4. Metadata are often cumbersome and full of gaps
  5. 5. Rich and well managed metadata make the difference and become a strategic part of your success!
  6. 6. Descriptive Technical Commercial Title, synopsis, duration, type, topics, cast, crew, genres, saga, collections, celebrities, age rating, year, country, languages, images… Schedule times, channels, duration, resolutions, encoding profiles, channel lineups, backend systems… Business model, price, margin, constraints on availability, bundles, packaging, allowed discounts… Metadata we are talking about Timeline Scene-level tags, start of closing credits, actor/ character in scene, product placement…
  7. 7. Metadata is not a silo
  8. 8. Areas Impacted by Metadata User Interface Personalization UI Autopilot Analytics Audience Profiling Catalog Consolidation Viewership Forecasting Advertising User Engagement Content Acquisition
  9. 9. Examples: Personalization Similar content Related content Explanations Developing stories Next-to-play Celebrity videos (teens love this) Character-based (for kids) Surfacing sports content Collections Recommendations News topics
  10. 10. UX ENHANCEMENT Next-to-play More playbacks, more ads Welcome back Shorter time-to-content Similar or related content Better catalog perception Because you liked More trust Targeted promotion Better conversion Personalized e-mail Higher email open ratio Personalized notification Higher return ratio IMPACT ON SERVICE KPIs and many more!
  11. 11. Examples: User Interface Data gaps ➜ Graceful degradation to fallback values Delayed enrichment ➜ Fast lane and incremental re-publishing Lack of links ➜ Use a knowledge graph Modification rights ➜ Lock data fields by source I S S U E S C O U N T E R M E A S U R E S Publishing rights ➜ Select data source by UI context
  12. 12. ➜ Link UI words to core metadata values But Search must work with user’s language! MD.genre = “action” CERCA azione LANGUAGE en it fr You can deliver a rich and solid core of metadata based on standardized language and reference dictionaries (natural language processing, dictionaries or other techniques) Simplest example Examples: Search
  13. 13. Word localization, synonyms, misspelled words, nicknames… Semantic approach is mandatory for natural language & voice search Include a self-tuning set of relevancy boosters ‣ a show that is hot today may be irrelevant in a few months or weeks ‣ immediate boost may be needed for news clips related to a developing story ‣ external excitement factors or trends may be needed by sports content Learn from audience behavior ‣ whether to use search or not depends on user’s habits and content type ‣ search is used in 3-10% of TV sessions; 18-25% on PC/tablet/phone (entertainment apps) ‣ voice search helps but most users name specific entities (titles, person names, character names, channels, genres, topics) ‣ use a search-focused analytics dashboard Examples: Search
  14. 14. LIVE EVENT VIDEO CLIP APP LIVE EVENT EPG AppsSports Highlights LIVE EVENTLIVE EVENT RELATIONSHIPS Channel (e.g. ESPN) Organization (e.g. NBA) Sport (e.g. Tennis) Tournament (e.g. World Cup) Sponsor (e.g. Nike) RELATIONSHIPS Match Athlete Sponsor Examples: Cross-domain Recommendations
  15. 15. Examples: Dynamic Streams Personalization Content in a flat list. No visual help to process so many things on screen. Collections and micro-genres: easily scannable
  16. 16. + Uplifting movies about teamwork American movies starring Tom Hanks French comedies set in Paris Collection metadata Collection metadata Collection metadata User Profile+ + MATCH POSITION 3 5 - Examples: Dynamic Streams Personalization
  17. 17. Handling Metadata
  18. 18. Handling Metadata 1. Blending data sources 2. Making data richer 5. Publishing changes faster 3. Automating workflows 4. Validating automatic operations
  19. 19. Content item MD.Netflix OFR.Netflix MD.Commonsense MD.Rovi OFR.Catchup MD.RottenTomatoes MD.Gracenote OFR.HBO-Go Multiple Data Sources: Reconciliation Data + Offer (playable) “Data Only” Sources OFR.EPG
  20. 20. Enrichment (Source Blending) Title Synopsis (en) Parental rating Topics Mood Genre Duration Title (en) Genre Year Parental rating Critics score Parental rating Parental advisory Title Review Duration Title (en) Critics score Topics Mood Title (fr) Synopsis (fr) Title (fr) Synopsis (en) Synopsis (fr) Parental rating Parental advisory Year Images Images 1 2 3 4 5 Content feed Licensed source Critics Review Parental Ratings Other Language
  21. 21. Handling Metadata 1. Blending data sources 2. Making data richer 5. Publishing changes faster 3. Automating workflows 4. Validating automatic operations
  22. 22. From Fragments to a Graph of Knowledge Movie Episode Gossip Video Talk Show Clip spouse 2015.. spouse 2000..2005 Gossip Video appearsIn appearsInactorOf appearsIn Season Series Special spinOff appearsIn Channel BrandTalk Show Brand Movie sequelOf franchise James Bond franchise Schedule interviewedIn
  23. 23. Semantic Reasoning: Properties appearsInactorOf interviewedIn hosts directorOf writerOf isContributor producerOf More specific More generic
  24. 24. Works with “inconsistent” tagging! Movie Episode Gossip Video Talk Show Clip spouse 2015.. spouse 2000..2005 Gossip Video appearsIn appearsInactorOf appearsIn Season Series Special spinOff appearsIn Channel BrandTalk Show Brand Movie sequel franchise James Bond franchise Schedule interviewedInproducerOf
  25. 25. Person Actor AthleteQuarterback Musician Agent Entity Organization Music Label Semantic Reasoning: Types Rapper CharityBand Apparently inconsistent tagging can still generate global value.
 No re-tagging needed
  26. 26. Team Venue Association (NBA…) Tournament (World Cup) Match Sport Athlete EPG Event Sports data structure Excitement Factor (external signal) Clip Sponsor VOD Asset University
  27. 27. Handling Metadata 1. Blending data sources 2. Making data richer 5. Publishing changes faster 3. Automating workflows 4. Validating automatic operations
  28. 28. Knowledge Factory Ingest Reconcile Deduplicate Enrich Validate Consolidate Export Knowledge Factory Data Source Data Source Data Source Metadata Core Editorial and Operational Tools Typical Workflow
  29. 29. Handling Metadata 1. Blending data sources 2. Making data richer 5. Publishing changes faster 3. Automating workflows 4. Validating automatic operations
  30. 30. Validation Operations can be validated automatically using “confidence” Human editors validate the operation or correct it The metadata management system should learn the correction and will apply it to similar situations in the future. Automatic operation CONFIDENCE HI LO Publish Ask human Publish
  31. 31. Handling Metadata 1. Blending data sources 2. Making data richer 5. Publishing changes faster 3. Automating workflows 4. Validating automatic operations
  32. 32. TIME Basic data arrives Quickly published to clients Data gets enriched Enriched data re-published to clients (via API) The Colbert Report 8:00 PM - Comedy Central Hi-def show image Guest celebrity Frame from the show Data ingestion Video clips generated Catch-up version created Fast lane and continuous data re-publishing Additional metadata Timeline tags PRODUCT PLACEMENT CELEBRITY CLOSING CREDITS ADS 1:25 4:20 6:00 9:30
  33. 33. Thank you! www.contentwise.tv For more information on Knowledge Factory, 
 UI Autopilot and Personalization, 
 please visit our website or contact us Digital TV. Personalized. info@contentwise.tv

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