Broadband TV and Recommendation: Improving the Customer ExperienceIan Kegel

Future Consumer Applications and Services Practice

BT Research & Technology

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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

• The Netflix effect: over 400 Netflix-ready 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

2011 2012 2013 2014

Internet-enabled TVs, STBs, Media Players, Games Consoles

Non Internet-enabled TVs and STBs

100M

400M

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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?

November 2011Major European TV Content Providers are integrating recommendation within their proposition.

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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 ProjectEU-funded Collaborative Project, 2008-2010

www.mymediaproject.org

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Application Programming Interface

External Application Integration and UI

Recommendations System Data

Core Software Framework

Object Relation Mapping

• Goal: To make it easier for Content Providers to take advantage of state-of-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.

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MyMedia Today

More information: http://www.ismll.uni-hildesheim.de/mymedialite/index.html

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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: Broadcast TV is still king… for now

• The UK video rental market is small: less than 3 videos per household per year

• OTT VOD stores have achieved low take-up (eg. Lovefilm is streamed by < 1% of UK households)

• But there are disruptors on the horizon: YouView, Netflix, Apple TV

• 83% of viewing is still live broadcast ‘linear’ TV

• 64% of ‘non-linear’ viewing is Catch-up TV (eg. PVR, VOD)

• UK more linear than US, with focus on fewer higher quality channels

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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

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04:00 08:00 12:00 16:00 20:00

1 Tuner Open/Closed2 Started watching channel or recording3 Stop/Start recording program4 Play, Pause, Rewind, Fast- forward, etc5 Delete Recording6 Schedule/Cancel Series Recording

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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

Customers ordered by VOD viewing frequency

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Implicit Feedback from VOD viewing alone is insufficient for the majority of the population.

Implicit Feedback from VOD viewing alone is insufficient for the majority of the population.

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Managing Implicit Feedback

Customers ordered by VOD viewing frequencyCustomers ordered by PVR recording frequency

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Implicit Feedback from PVR recordings improves prediction

for more customers.

Implicit Feedback from PVR recordings improves prediction

for more customers.

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Managing Implicit Feedback

Customers ordered by VOD viewing frequency

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Could we be smarter about collecting implicit feedback?Could we be smarter about collecting implicit feedback?

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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: 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 Twitter56% use Facebook34% use other mobile applications

– 30% said it was "fun" and made TV "more interesting".

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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

A New Type of Feedback?

Social

• Can be high quality• Both positive and negative• Can be explicit or implicit

(and difficult to interpret)• Potentially very noisy• Not always available

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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?

Broadband TV and Recommendation: Improving the Customer Experienceian.c.kegel@bt.com