Broadband TV and Recommendation:
Improving the Customer Experience
Ian Kegel
Future Consumer Applications and Services Practice
BT Research & Technology
How can we make TV Recommendation
work in a connected, multi-device world?
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Answers?
1. Use a flexible, high performance
Recommendation System
2. Make better use of Feedback
3. Incorporate Social Media
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Future TV: Multi-platform, Multi-device
• Unified access to live broadcast, VOD,
Catch-up TV, OTT and local content
• 20M PS3, Xbox360 & Wii consoles in the UK
can deliver on-demand content to the TV today
• 69% of UK households forecast to have
Internet-enabled TVs by 2014
400M
Internet-enabled TVs, STBs,
Media Players, Games Consoles
Non Internet-enabled
TVs and STBs
100M
2011
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2012
2013
2014
• The Netflix effect: over 400 Netflixready devices in the US, and now
coming to the UK…
• OTT-exclusive ‘Internet TV’ players
emerging (such as Apple, Boxee and
Google TV)
• Warner OTT content now integrated
with Facebook
Why Recommendation?
• Huge amounts of content are available – but customers
face rapidly increasing difficulty in finding what they want:
the “crisis of choice”
• Recommendation enhances the customer experience by
anticipating customers’ preferences and enabling new
forms of interaction.
• It also enables the provider to promote specific
content, and can reduce the cost of delivery by driving
pre-emptive delivery techniques.
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Why Recommendation?
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Recommendation Challenges
• Satisfying new customers who have yet to purchase
anything
• Suggesting new content when few people have
already purchased it
• Integrating different catalogues from multiple
providers
• Making the customer experience clear and simple
– Addressing individuals as well as groups
– Knowing who is watching at a given time
– Using implicit and explicit feedback appropriately
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The MyMedia Project
www.mymediaproject.org
EU-funded Collaborative Project, 2008-2010
External Application Integration and UI
Core Software
Framework
System Data
Object Relation Mapping
System Data
Relational Database
Metadata Enrichment
& Catalog Import
Interface
Application Programming Interface
Pluggable Metadata
Enrichment & Catalog
Information
System Data
Catalog Information/Enrichment
Recommendations
Algorithm Interface
Recommendations
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Pluggable
Algorithms
• Goal: To make it easier for Content
Providers to take advantage of stateof-the-art recommendation systems.
• Flexible and modular Core Software
Framework allows algorithms, content
catalogues, feedback sources and UI
components to be plugged in.
• Library of recommender algorithms can
be hybridised in the most appropriate
way for the application.
• Four real-world trials: IPTV, catch-up
TV, e-commerce portal, user-generated
content.
MyMedia Today
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More information: http://www.ismll.uni-hildesheim.de/mymedialite/index.html
Answers?
1. Use a flexible, high performance
Recommendation System
2. Make better use of Feedback
3. Incorporate Social Media
© British Telecommunications plc
Future TV: Broadcast TV is still king… for now
• 83% of viewing is still live broadcast
‘linear’ TV
• 64% of ‘non-linear’ viewing is Catchup TV (eg. PVR, VOD)
• UK more linear than US, with focus
on fewer higher quality channels
• The UK video rental market is small: less
than 3 videos per household per year
• OTT VOD stores have achieved low takeup (eg. Lovefilm is streamed by < 1% of
UK households)
• But there are disruptors on the horizon:
YouView, Netflix, Apple TV
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Types of Feedback
Explicit
• High quality when given
• Usually based on ratings
• Both positive and negative
• End-of-scale bias
• Not always available
Implicit
• Abundant, theoretically
• Based on observations
• No negative feedback
• Inherently noisy
• Need to model both
preference and confidence
Hu, Koren and Volinsky (AT&T Labs): “Collaborative Filtering for
Implicit Feedback Data Sets”, ICDM2008
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An Example
1
2
3
4
5
6
6
Tuner Open/Closed
Started watching channel or recording
Stop/Start recording program
Play, Pause, Rewind,
- Fast forward, etc
Delete Recording
Schedule/Cancel Series Recording
5
4
3
2
1
0
04:00
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08:00
12:00
16:00
20:00
Dynamics of TV viewing behaviour
Taxonomy of the TV viewing process (Bilandzic, 2004)
• Scanning: Deliberate, heuristic evaluation of a channel
• Flipping: Scanning all available channels
• Grazing: Systematic, slow evaluation of a channel
• Zapping: Switching to avoid certain content (eg. commercial break) then returning
• Hopping: Continuous switching back and forth between two or more programmes
Wonneberger, Schoenbach and van Meurs (Univ. of Amsterdam):
“Dynamics of Individual Television Viewing Behavior: Models,
Empirical Evidence, and a Research Program”, AEJMC2008
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Managing Implicit Feedback
VOD views per month
100
Implicit Feedback from VOD
viewing alone is insufficient for
the majority of the population.
10
0
O(1M)
Customers ordered by VOD viewing frequency
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Managing Implicit Feedback
500
Implicit Feedback from PVR
recordings improves prediction
for more customers.
10
0
50
O(1M)
Customers ordered by VOD viewing frequency
Customers ordered by PVR recording frequency
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PVR recordings per month
VOD views per month
100
Managing Implicit Feedback
500
Additional events captured
per month
VOD views per month
100
Could we be smarter about
collecting implicit feedback?
10
0
O(1M)
Customers ordered by VOD viewing frequency
© British Telecommunications plc
Answers?
1. Use a flexible, high performance
Recommendation System
2. Make better use of Feedback
3. Incorporate Social Media
© British Telecommunications plc
Future TV: a ‘Social Experience’
• Second screen interaction during TV
viewing is becoming the norm
• The TV remote is being replaced by a
keyboard, tablet or smartphone enabling
better interaction
• Second screens provide access to EPG
and PVR functionality
• Increasingly used for enhanced
programme interaction and participation
• 80% of mobile Internet users under
25 regularly use their device to
comment or chat while watching TV:
– 72% use Twitter
56% use Facebook
34% use other mobile applications
– 30% said it was "fun" and made TV
"more interesting".
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A New Type of Feedback?
Explicit
•
•
•
•
•
Implicit
High quality when given
Usually based on ratings
Both positive and Social
negative
End-of-scale bias
• Can be
Not always available
•
•
•
•
© British Telecommunications plc
•
•
•
•
quality
•
Abundant, theoretically
Based on observations
No negative feedback
Inherently noisy
Need to model both
preference and confidence
negative
high
Both positive and
Can be explicit or implicit
(and difficult to interpret)
Potentially very noisy
Not always available
Some Challenges
• What techniques can be used to manage implicit
feedback in real-world systems?
• How should social feedback be balanced with the
traditional explicit and implicit forms?
• What impact will new forms of second screen
interaction have on content personalisation?
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Broadband TV and Recommendation:
Improving the Customer Experience
ian.c.kegel@bt.com