There is too much online content which makes it difficult to identify quality content. Current content recommendation systems rely on popularity and behavioral filtering, but this favors established contributors over new voices. A new system called IntelliButler aims to address this by using machine learning to analyze the substance of content itself, allowing it to identify valuable niche content and help build reputations independent of existing popularity or social connections.

1. THE FUTURE OF CURATED
CONTENT
Chicago Tech Showcase  Webinar
Thoughtly Founder  Chase Perkins
Webinar Host  Zhu-Song Mei
08-27-14

2. Every person is a content creator,
and every platform is a method of content
distribution.
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3. NOISE PROBLEM
There is so much content online that we
created context-specific social networks to
silo content by type.
Academia and Mendeley for academic scholarship.
LinkedIn to publish content in a professional context.
Facebook for friends and family.
Medium for musings.

4. NOISE PROBLEM
But as the context-specific platforms grow,
they too have a noise problem.
Even though we’ve isolated the content in
silos, we’re still having problems identifying
content at scale.

5. “A common complaint from Facebook users is that the news feed is
filled with junk that they don’t want to read from news sites known for
pumping out “listicles” featuring subjects like humorous cat videos or
celebrity gossip.”
WSJ, August 25, 2014, Facebook is Cracking Down on Click-Bait –
“What people don’t realize though is that the luck of going viral was
based on a mountain of hard work, on years of effort. There’s a
frustrating truth to success in the Internet age: in order for your work to
reach an audience, someone with power has to give it a chance, and in
order for someone in power to give it a chance, it has to have an
audience.”
Wired, August 25, 2014, Want to Go Viral? It’ll Take a Lot More Work
Than You Think –
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6. “On the Internet, there is no limitation to the
number of outlets or voices in the news chorus.
Therefore, quality can easily coexist with crap.
All can thrive in their respective markets. And,
the more noise, confusion, and crap – the more
there is an increase of, and corresponding
need for, trusted guides, respected experts,
and quality brands.”
Marc Andreesen, The Future of the News
Business: A Monumental Twitter Stream all in
One Place –

7. PROBLEMS IN CONTENT FILTERING
Most content recommenders are based on
collaborative filtering, meaning they use
either…
Popularity-Based Filtering
Good for identifying gossip
and headlines – stuff that’s
trending.
Behavioral Filtering
Finds what your friends and
people like you tend to like.
OR

8. BUT THERE IS A FUNDAMENTAL
PROBLEM WITH COLLABORATIVE
FILTERING:
There are very few data points and user
interactions associated with new or niche content.

9. TIMMY VS. SARAH
Timmy has been called a “thought leader” on a social platform.
Sarah is a busy graduate student.
Timmy and Sarah might write an identical piece.
In theory, they should stand an equal chance of being
recognized and gaining traction.
But Timmy is going to win out almost every time.
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10. In order to identify quality content at scale, you
need a system that looks at the content itself.
You need a system that reads the content it
recommends.

12. INTELLIBUTLER
A new breed of content recommender and
scheduling mechanism.
Powered by a contextual machine learning
natural language processor.
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13. INTELLIBUTLER
Examines several hundred sites and
sources per day, as well as all content
submitted by the IntelliButler community for
analysis.
Recommends content based on its
substance, allowing you to consistently
identify and share great content and carve
out a reputable niche in your field.
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