The document discusses the importance of well-managed metadata for personalization in digital media, highlighting various types, including descriptive, technical, and commercial metadata. It emphasizes the necessity for automation in metadata workflows, effective user engagement strategies, and the integration of rich metadata to improve user experience and service performance. Additionally, it outlines the challenges faced in metadata management and the importance of adaptive search capabilities to cater to user preferences.

1. The Personalization Toolkit
Metadata Essentials
Web Session, September 2015
Pancrazio Auteri, CTO Kauser Kanji, Editor

2. Well-managed metadata is critical

3. Metadata: a lot of manual work

4. Metadata are often
cumbersome
and full of
gaps

5. Rich and well managed metadata
make the difference
and become a
strategic part
of your success!

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. Metadata is not a silo

8. Areas Impacted by Metadata
User Interface
Personalization
UI Autopilot
Analytics Audience Profiling
Catalog
Consolidation
Viewership
Forecasting
Advertising
User Engagement
Content Acquisition

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. 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. 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. ➜ 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. 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. LIVE EVENT VIDEO CLIP APP LIVE EVENT
EPG
Apps
Sports
Highlights
LIVE EVENT
LIVE 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. 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. +
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. Handling Metadata

18. Handling Metadata
1. Blending data sources
2. Making data richer
5. Publishing changes faster
3. Automating workflows
4. Validating automatic operations

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. 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. Handling Metadata
1. Blending data sources
2. Making data richer
5. Publishing changes faster
3. Automating workflows
4. Validating automatic operations

22. From Fragments to a Graph of Knowledge
Movie Episode
Gossip Video
Talk Show
Clip
spouse
2015..
spouse
2000..2005
Gossip Video
appearsIn appearsIn
actorOf appearsIn
Season
Series
Special
spinOff
appearsIn Channel
Brand
Talk Show
Brand
Movie
sequelOf
franchise
James
Bond
franchise
Schedule
interviewedIn

23. Semantic Reasoning: Properties
appearsIn
actorOf
interviewedIn
hosts
directorOf
writerOf
isContributor
producerOf
More specific More generic

24. Works with “inconsistent” tagging!
Movie Episode
Gossip Video
Talk Show
Clip
spouse
2015..
spouse
2000..2005
Gossip Video
appearsIn appearsIn
actorOf appearsIn
Season
Series
Special
spinOff
appearsIn Channel
Brand
Talk Show
Brand
Movie
sequel
franchise
James
Bond
franchise
Schedule
interviewedIn
producerOf

25. Person
Actor
Athlete
Quarterback
Musician
Agent Entity
Organization
Music Label
Semantic Reasoning: Types
Rapper
Charity
Band
Apparently inconsistent tagging can still generate global value.
No re-tagging needed

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. Handling Metadata
1. Blending data sources
2. Making data richer
5. Publishing changes faster
3. Automating workflows
4. Validating automatic operations

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. Handling Metadata
1. Blending data sources
2. Making data richer
5. Publishing changes faster
3. Automating workflows
4. Validating automatic operations

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. Handling Metadata
1. Blending data sources
2. Making data richer
5. Publishing changes faster
3. Automating workflows
4. Validating automatic operations

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. Thank you!
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UI Autopilot and Personalization,
please visit our website or contact us
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