>> So when we think about recommenders?
We could define them as tools to help identify worthwhile stuff for people.
And we can break them down in one sense along the interface.
Filtering interfaces take a stream of content like email or
news articles and identify the ones that you want.
I happen to use filtering interfaces all the time in
my email because I get email messages from my department head.
And if I spent all my time reading them,
I might not have time left to realize that the former finance minister of
some African Nation wants to help me transfer $6 million to my account.
And I'd hate to miss that opportunity just because I was getting work done.
So, I use email filtering interfaces, but we also have recommendation interfaces.
These can be suggestion lists or top ten lists but
more commonly they are placements.
I go to a website like Amazon and a bunch of products are showing up there.
They've been recommended.
If I use my set top box for cable,
Comcast shows me a few movies or shows that it thinks I might want to watch.
Or if I go into a store, I may get a coupon printed out.
That's a recommendation for a product that they think and
perhaps hope I will purchase.
Finally, we have prediction interfaces.
This is what we saw in the use net example but there are a number of cases where what
you want is some sort of a score like a predicted rating.
If I'm looking at travel reviews, I might expect a site like TripAdvisor to tell me,
this is a four and a half star, or in their case, four and a half circle hotel.
It's a place we think you are really going to like.
>> So as we go throughout these courses and
we discuss more about recommender systems and their algorithms.
It will help to have some vocabulary
that we're going to use to talk about the systems.
And a rating, we're going to use this term a lot and
it's an expression of preference.
These ratings come in two forms,
there's an explicit rating which is something the user articulates.
The user says, I like the Iron Giant four and a half stars.
That is the user's explicit expression of how much they like that particular movie.
But there's also implicit ratings that are inferred from user activity.
If I'm watching Netflix and I start watching a movie and
five minutes into it, I stop watching it, and I never come back.
It's reasonable to infer from that,
that I probably didn't like what I saw in that movie.
There's many cases when we can infer from the user's behavior how much
they likely like a particular product.