Hi and welcome back to data science in minutes I'm Blaire and in this video
we'll be learning about recommender systems. So what do you think of when you
think of recommendations? My friend recommends that I watch "Stranger Things"
and my sister recommends that I watch "Ozark" and my mom recommends that I see
this new documentary on "Planet Earth". Are these recommendations based on what
they like or what I like? And what if I want to watch something different that
none of those three recommended? Wouldn't it be great if someone just understood me?
Well that's where recommender systems comes in
So what really is a recommender system?
A recommender system is really an automated system to filter some entities
these entities can be any products, ads, people, movies, or songs and we see this
from all over on a daily basis from Amazon to Netflix to Pandora to YouTube
to eHarmony.
For example, we watch a movie and then later on we get a
recommendation for a different movie based on the power of previous viewing
history it could also be a product that we bought and then we get a
recommendation for another product based on the previous product viewing or
purchase history and the recommender doesn't work only in what products we
are being shown, but also in what order the products are being ranked.
So why are recommender systems being built?
Businesses are showing us recommendations
and relevant content for a couple of reasons. For one, most
businesses think they understand their customer, but oftentimes customers can
behave much differently than you would think so it's important to show the
users what is relevant to them while also sharing new items they would be
interested in
Recommender systems also serves to help us solve the information
overload problem and helps us narrow down the set of choices and for
businesses they get the benefit of selling more relevant items to the user
It is also there to help you, the customer, discover new and interesting
things and to help you save time and from a business perspective it helps to
better understand what the user wants
So how does it work?
Let's say you like
cheeseburgers sans tomatoes.
So how does your mom know whether or not you like
tomatoes on your burger? She could have asked you specifically if
you want Tomatoes or she watched you pick them out every time you order a
burger.
A recommendation engine works the same way. It will either ask you what you
want or ask if the content was relevant, look at other users with similar
behavior, or study your activity. And even if your mom knows you well maybe a
machine learning algorithm knows you better.
For example, when we go to Netflix
or any other service that relies on recommendations the first time when we
go there they will ask you what are your taste preferences and there is a reason
for that. Because if they do not know what your taste preferences are at all
it's a "cold start problem" they have no idea and they have no profile for you
and they will force you to at least put in something. What movie do you like and
so on because how would they know otherwise? So let's say I place an order
through Amazon Prime for bananas now I'm being shown a banana slicer in the
recommendations to me.
This banana slicer has over 5,000 reviews in a five-star
rating. User ratings, number of reviews, and relevancy can play a factor in terms
of what is being recommended to me.
Another example of this is how YouTube's
recommendations work. Videos that have a lot of watch time, engagement, is relevant
to the topic I'm searching, and is a relevant topic based on my video
history will be shown to me. If I watch a trailer for "Avengers Endgame" I might be
shown a trailer for "Iron Man" or funny bloopers from filling the movie or an
interview with some of the actors. These algorithms are so smart that they are
able to decide what to show us and it can be scary accurate. It should also be
noted that each company might have their own algorithm and way of generating
recommendations and that what one company's method for applying
recommendations does not apply to all.
In our next video we will look at
the different types of recommendations and how they are applied. Give us a
thumbs up if you like this video, leave us a comment about the strangest thing
you've been recommended, and what topic you'd like to see covered in less than 5
minutes. For more tutorials check out
tutorials.datasciencedojo.com. Thanks for watching and we'll see you in our next
video