You Are What You Read
Can our reading
choices reveal something about ourselves?
““Tell me what you eat, and I’ll tell you what you are”
Brillant-Savarin”: Iron Chef
The renowned scientist Jean Savarin
once said, “Tell me what you eat, and I’ll tell you what you are”. We can
extend this notion outside of food, to anything we consume. Whether it’s food,
music, or what we read. Our choices reflect our preferences. Companies like
Amazon, Netflix and Spotify use preferences to make predictions. These predictions
are used to market to you what you may like in the future. The goal of making
these predictions is to help provide better customer experience. For example,
to make a better song recommendation on Spotify. Or to sell an additional
product on Amazon. Then, can we say the same for our reading behavior. Namely,
can we know a person better based on what they read?
Imagine this classic analogy, you
are in a supermarket. The aisles are content topics, and your basket is what
you choose to read. From news, sports to fashion, you browse this supermarket.
Picking the stories and articles that you wish to read. and put it in your
basket. Your basket represents your personal combination of preferences. By
combining these preferences, allows for grouping of preferences. These grouped
preference, can used to build audience affinities. These audience affinities
happen naturally, as people tend to seek groups that share their interests. And
uncovering these audience affinities is a common practice in data science.
For example, Spotify or any other
music service, has a record of every song you have listened to. Spotify also
keeps a record of everyone else and their musical history as well. Music services like Spotify know that a happy
customer is more likely to stay a customer.
To make their audiences happy, and keep their product ‘sticky. ‘Spotify has to keep their product relevant
to their customer. To be relevant, they aim to expand the user’s musical taste.
To do so, they first try to find users with similar music tastes to build a
music segment. With this segment, they
find other music that was listed to by the group. They use this selection of
music to recommend to the group. These recommendations expand as new music exposure
to their users. Spotify use recommendation engines to find similar music or
content you may like.
Here is an example of a recommendation
system. Say I like sports articles, and
Jack and Diane also like sports articles.
Jack and Diane also like gossip news. If Jack and Diane are similar to
me, then I also may like gossip news. Of course, it depends on how similar
these groups are. If we were able to hone into deeper detail, we can unlock
even more data potential. Instead of sports articles, I was reading hockey news
for the Pittsburgh Penguins. Then, I would have to find people who were also
into the Pittsburgh Penguins. Once I found those users, see what other topics
they read. This method unlocks latent
information from similar audiences. The topics that Jack and Diane like outside
of our shared love of the Pittsburgh Penguins, I also may like. This
methodology is known neighborhood approach. A technique that is commonly used
in recommendation systems.
While we won’t go into the math
behind this. We can assume that these method helps managing a very large data
problem. To apply it to our reading scenario. We simply look all the traits of
articles a person reads, apply our recommender system, and out comes the
results. This application can be used to fostering a deeper connection with a
audience.
Publishers can utilize recommendation engines, to discover
latent traits of their readers. By doing
so, they have the potential to unlock a wealth of data opportunities.
One use would be to automatically
critique and serve content based on my interests. Another use may be to display a relevant
sponsored message. Nevertheless, publishers can use this to make their product
more 'sticky'. Just like the shows we watch, or music we listen to. Optimizing
articles based on past preferences can elicit a better user experience. By
doing that, you are indeed, what you read.
[i] Deep Neural Networks for
YouTube Recommendations: https://static.googleusercontent.com/media/research.google.com/ru//pubs/archive/45530.pdf
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