Beat Netflix at Its Own Game - The New Generation of Recommendation Technology - Simply Relevant
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Beat Netflix at Its Own Game
The New Generation of Recommendation Technology
Simply Relevant
BEAT NETFLIX AT ITS OWN GAME : THE NEW GENERATION OF RECOMMENDATION TECHNOLOGY
Introduction
The growth and success of Netflix is closely
followed by executives in the global media
business. Every cable and satellite TV oper-
ator and video-on-demand (VOD) platform
scrutinizes Netflix to better understand
the secret to its success. Recommendation
technology and user experience have been
central to Netflix’s rapid growth but its
recommendation engine’s proprietary
design, high cost and user experience
limitations have created opportunities for
newer third party recommendation tech-
nologies that do a better job at a fraction
of the cost.
•2•
BEAT NETFLIX AT ITS OWN GAME : THE NEW GENERATION OF RECOMMENDATION TECHNOLOGY
The Netflix Competitive Challenge
Netflix is already used in one third of US and
Canadian households and continues to grow.
With its initial introduction of DVD rent-
als in 1999 and streaming service in 2007,
it has sent shock waves through the video
distribution industry, crushing competitors
like Blockbuster along the way. In a sign of
desperation, competing US cable operators,
who also provide internet to the home,
were throttling Netflix’s speed to degrade
its service but are now prohibited from
doing this by the recent FCC Net
Neutrality decision. The prospect of
Netflix entering new international markets
Variety, Jan 2015, “Netflix Tops 57 Million Subscribers...”
has left many local incumbent
broadcasters, cable companies and VOD
service providers anxious about the new
competition and scrambling to upgrade
their offerings.
Why is Netflix s o f eared? B eyond i ts l ow
How Netflix
monthly all-you-can-eat pricing and deep
content library, it is its strong discovery and
handles Content
recommendation experience that is credited
the most.
Recommendation
Netflix has spent more than a decade refin-
According to Neil Hunt, Netflix’s ing its recommendation solution, evolving
Chief Product Officer, the from a system that uses statistical analy-
company employs 300 people and sis of viewing events to predict behavior-
spends $150 million a year on al patterns (collaborative filtering), to one
that combines human curation and metada-
discovery and recommendation.
ta creation with proprietary algorithms to
offer truly personalized recommendations.
This has produced a sophisticated recom-
This is sometimes called “content-centric”
mendation engine that is able to promote
recommendation because it uses metada-
a large percentage of Netflix’s catalog to a
ta and algorithms to recognize the diverse
broad cross section of viewers based on their
and subtle themes of each film or TV show
individual tastes. Netflix estimates that almost
to provide better suggestions based on
75% of what its viewers watch is driven by its
thematic similarities.
recommendation engine.
•3•
BEAT NETFLIX AT ITS OWN GAME : THE NEW GENERATION OF RECOMMENDATION TECHNOLOGY
Netflix recommends their own and popular content above personalized content.
All of this great technology does not always it still hasn’t found a solution to the discov-
make up for the fact that Netflix’s library is erability problem.” Part of this is likely due
mostly older, long-tail content (especially to Netflix not always meeting with users’
outside of its home market, the US), and its expectations as it increasingly gives prior-
interface leaves many viewers dissatisfied. A ity recommendations to its own content,
recent Fast Company article, “The World’s which further serves to break trust if it isn’t
Most Overrated Interface Design”, noted, “... relevant.
for all of its virtues, and its sizable library,
Problems with Netflix’s Approach and
Alternative Models: Natural Language Solutions
Netflix’s user interface does not always
adequately explain why it is making a partic-
ular recommendation, which results in some
confusing suggestions. After a few of these
recommendation “fails”, the viewer’s trust is
eroded and some will resort to bypassing it
and sifting through large numbers of titles to
find something to watch by themselves. This
increases frustration and resentment of the
service and has a negative effect on custom-
er loyalty. Nonetheless with its content-centric
approach, Netflix is able to create thousands of
sub-genres to please almost any taste but find-
Recommendations are not always explained
ing and exploring these subgenres is difficult.
•4•
BEAT NETFLIX AT ITS OWN GAME : THE NEW GENERATION OF RECOMMENDATION TECHNOLOGY
Although Netflix has helped to move recom-
mendation beyond collaborative filtering,
it still lags on the user experience, explain-
ing recommendations and making content
discoverable. A semantic solution, which
explains to users in natural language why
they’re being shown a recommendation
improves the experience of using an online
video service. It allows the viewer to quick-
ly understand key themes of the content
shown and why it is being recommend-
ed. The US VOD service, M-GO, suggests
recommendations that go beyond genres,
mixing emotional, factual and contextual
data on the users’ history and preferences.
For example, M-GO will identify that you
like to watch “Hilarious movies with irony
and satire” on Sunday afternoons and push
content like The Office or Zoolander specif-
ically on that day and time. It will also show
you other explicit facets of your viewing
profile in order to help you browse their
catalog according to your interests.
Recommendations that go
beyond genres to take into
account emotional, factual and
contextual data on the users’
Personalized recommendation interface based on
history and preferences. semantics
CanalPlay, a leading SVoD platform in France 2- CanalPlay tells you explicitly what
and direct competitor of Netflix, explains specific characteristics of any given
movie recommendations in every movie page recommended content fits with your profile.
of its “Suggest” section, which shows a single
recommendation based on a user’s previous The Tinder-like interface, based on basic swipe
viewing data. Suggestions are always built on and skip features, gives a recommendation
two levels: with key themes and details of why it is being
shown making it easy for a user to watch or
1- CanalPlay connects all personalized
move on to another recommendation.
recommendations with similar previously
watched content
•5•
BEAT NETFLIX AT ITS OWN GAME : THE NEW GENERATION OF RECOMMENDATION TECHNOLOGY
Canal+ Group: semantic recommendation with a Tinder twist – “Similar to Luther; a series with murder”
The "Discovery Spiral" The Benefits of Natural
Finally, there’s the problem of constantly being
shown the same recommendations based
Language Solutions
on earlier viewing habits – the so called From a user experience perspective,
« discovery spiral ». It is one of the main improvements in recommendations result
weaknesses of the Netflix platform. What if from a simple idea: technologies have
a user wants something different? A movie to reveal the reasoning behind recom-
that is outside their usual preferences? How mendation intelligence – using natural
can you rapidly find language that is
something to watch with easy to understand
a friend that has different – in order to provide
tastes than yours? trusted, always
The Spideo app, available personally relevant
on iPad, proposes a mood- and simple to under-
based discovery mode that stand suggestions.
provides an efficient
solution to these use From a digital TV
cases. It offers 20 “wishes” or VOD operator’s
that can be selected and standpoint, this
combined to discov- natural language
er content that will best approach results in
match your mood. If a user Mood board resolving the discovery spiral increased engage-
is interested in “New Horizons”, “Romance” ment, long- term loyalty and improved
and “Hope”, they just need to click on three catalog exposure which in return gives it
buttons and they are immediately shown the a very good reason to invest in content
most relevant movies that fit these criteria. acquisition.
•6•BEAT NETFLIX AT ITS OWN GAME : THE NEW GENERATION OF RECOMMENDATION TECHNOLOGY
Conclusion About Spideo
Netflix’s i mpressive g rowth h as s hown t hat a Spideo is a content recommendation and
video service with a strong focus on content analytics platform that uses semantic-based
discovery and recommendation innovation discovery to deliver personalized viewing
can win significant m arket s hare over i ncum- suggestions based on natural language,
bents and competitors with less sophisticated profile, and social trends.
approaches. Netflix is clearly taking a
better approach to recommendation, but it is At Spideo, we understand that the high-
far from perfect. Only content-centric, seman- est quality recommendations are trusted,
tic and explained recommendations can offer personally relevant and simple to under-
a fun and intuitive user experience. stand. The Spideo platform is designed to
mask the technical sophistication behind its
Video operators and distributors can now get recommendation intelligence resulting in a
better recommendation solutions than Netflix solution that is simple to use, deploy, operate
at a small fraction of the $150 million it spends and tune. Proven in the marketplace with Tier
each year. The new generation of content 1 service provider deployments in Europe
recommendation and analytics technology can and the United States, we deliver the most
help legacy operators level the playing field trusted and personally relevant recommen-
with Netflix and even beat it at its own game. dations, 100% of the time.
Simply Relevant
To learn more, please contact us :
Web : www.spideo.tv
Email : contact@spideo.tv
Twitter : @SpideoCorp
Tel : +33 9 81 92 82 99
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