Going Behind the Scenes of Your Content Recommendation Engine

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Going Behind the Scenes of Your Content
Recommendation Engine
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Going Behind the Scenes of Your Content
Recommendation Engine
Since its inception in 2005, YouTube has been a
rage. With a gargantuan library of videos,
YouTube accounts for two-thirds of the videos
that millennials are watching. A research study
conducted by Google on the online video trends
revealed that 6 out of 10 people choose online
video platforms over live TV. Based on this
study, Google predicted that by 2025, only half
of the viewers of less than 32 years of age will
subscribe to pay TV service. Screens are getting
smaller, internet is getting faster, user
expectations are growing bigger, AI is becoming
smarter and with it, the AI-based content
recommendation engine. Thanks to its ability of
delivering user-specific curated content
suggestions, more and more viewers are ditching
the traditional pay TV for online media
streaming. With a projected 40.7 Compound Annual
Growth Rate of AI-based recommendation engine
market by 2022, only 10 of people will watch
traditional TV and VR will become a key aspect of
television and video. Taking a cue from this
shift, content creators and media houses are also
trying to align their production, promotion, and
placement strategies with the evolving online
viewership. Content recommendation engines have
been around for quite some time now and are being
constantly improved and upgraded to deliver
services as per individual user preferences. The
real heroes of the film of recommendation engines
are, however, big data analytics and machine
learning with QA testing in a supporting but
integral role. It would be a dreary task for a
person to do, while it takes these AI-enabled
engines only a few seconds. Instead of letting
the user go through the pain of searching and
sifting through the gigantic content library, the
recommendation engines simplify the
decision-making process by optimizing the choices
as per the users desires and feelings. Not only
does this type of information filtering systems
enhance visibility of the content but also
ensures higher value to the viewers.
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Going Behind the Scenes of Your Content
Recommendation Engine
Writing the script As the media and
entertainment industry is becoming predominantly
digital, application of data science is growing
significantly. In order to improve the catalog
and provide personalized suggestions, the engine
has to work in tandem with users taste. While
developing a recommendation engine for modern
digital media platforms, it is critical to
transcend beyond the traditional collaborative or
content-based filtering approach. Simply relying
on buyers history or the past-liked or -disliked
content will not cut it anymore. Since ML feeds
on data, it is imperative to collect as much
information as possible. The level of accuracy
that a recommender system is able to present is
directly proportional to the amount of data
available on the users consumption pattern.
There is a need for a model that employs
analytics on the existing data to provide
contextual predictions while taking a users
geography, demography, search history, and
previous engagements into consideration. For a
recommendation engine to succeed among the
digitally-dedicated content consumers, it must be
accurate and empathetic to the user. Onboarding
the cast Most recommendation engines fail often
because they either lack the capability of
surprising their users with diversity or the
skill to build a positive image through relevant
content suggestions. These systems should be able
to perform optimal customer sentiment analysis
for comprehending their past behavior and make
the future judgements accordingly. From social
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Going Behind the Scenes of Your Content
Recommendation Engine

media statistics and users media content usage
to the trending topics and seasonality,
everything has to be in the picture while
designing a content recommender system. When all
this data is taken into consideration, analytics
is able to develop each users profile and match
it with the content that has the highest
probability of being liked by that user. Through
this user-content match-making, big data
analytics enable the media and entertainment
industry to enhance their targeting abilities by
a carefully-planned, continually-evolving,
well-thought content distribution strategy. With
the involvement of trends into the data set, the
recommender system is able to present a more
diverse assortment of choices. By taking a bold
approach of suggesting content that is entirely
different from the users existing profile, the
recommendation engine establishes its credibility
in content-related decision-making. Read Full
Blog at https//www.cigniti.com/blog/ai-ml-conte
nt-recommendation-engine/
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