This is the paper that became popular during the Netflix competition,

the PMF paper.

And GPMF is a variant that we cooked up, which is a generalized version of

PMF which can take into account these additional dimensions and factors.

So in this particular comparison, as we are increasing the rank.

We are showing how the performance improves where you have,

just like Joe said, that you have the ratings matrix.

And you also have the movie versus cast matrix and

you're trying to sort of capture that.

So as you give more rank,

as you increase the rank you can see that the performance of this more fancy model.

The GPMF actually keeps improving because it can take advantage of this additional

information that's available in the cast and it can align that and

can understand what types of movies the user likes.

What kinds of people work in similar types of movies and so on.

So there are been sort of a parallel development in machine learning,

data mining, and other areas.

So if you look for things like collective matrix factorization,

there's a whole bunch of models that go by that name.

Which try to accomplish similar things.

And you should be able to find, you know, enough ideas on how to this can be done.

But this is just one such idea that we worked on.

And it definitely showed improvement.

>> Well it's neat, because it shows both things on the same graph.

It shows one that this is always better.

Even though we're not doing the single perfect metrics factorization,

having more data gives us a better result.

>> Right. >> But what we're also showing is that

the total amount of information in the system Is increasing because

we're able to take advantage of more dimensions.

>> Right. >> And

obviously what PMF is showing is it gets worse is that it’s over fitting.

>> Yes, exactly.

>> Because it's used all the information it could possibly use.

>> Exactly, its sees the [INAUDIBLE].

>> So the one other thing that comes out nicely from this is a little bit better

hope of having some forms of content understanding and bring up both of these.

From your work you had talked about how you could find cast clusters by

identifying clusters of cast members.

That had nearby vector representations, >> Right.

>> Of this representation.

>> Right.

>> Can you tell us a little bit about that?

>> Sure, yeah, so

in the example that we are talking about you have a user by movie matrix.

Which is like usual ratings matrix and the movie by cast matrix,

which is sort of the new thing we threw in and

then we are sort of looking at the joined factorization.

So you are going to get this latent factor representation for every actor or actress.

And then you can cluster them based on these five dimensional or

ten dimensional representation.

You can do just run around something like [INAUDIBLE] or

do something more fancy to find clusters among the actors.

So we looked at some of the results and some of it made sense so

we found one cluster where mostly the science fiction actors got together

a lot of Star Trek, you know Apollo 13.

Ed Harris and Nimoy and so on.

All of them grouped together.

We found another cluster which is largely actors in the 40s through 60s.

You know, Cary Grant, Humphrey Bogart and people like them.

So so some of these actually,

if you carefully start poring over the results, they make more sense and

there's a better interpretability as to what may be going on over here.

>> Well, part of the thing that makes this kind of analysis neat is that

it can sometimes help you either back or refute intuitions.

And so- >> Yeah.

>> You look at the bottom there and

Paul Newman actually did most of his acting after many of those

people had stopped. >> Right.

>> But there's this sense that he was a throwback to the type of actor

of an earlier era.

And what we can see is that if you look at the data of the movies

that are liked by people and the actors that are in them.

When we bring all of this together sure enough.

He sits there along side Cary Grant and

Humphrey Bogart as opposed to some of the actors who might have been later and

part of a different generation stylistically.

>> Yeah, that's true.

>> And so none of this necessarily means that you have

an easy time again describing what the dimensions are.

>> That is true.

>> That's always going to be a challenge with matrix factorization techniques.

But you might have an easier time explaining

some attributes of a movie that somebody would like because

you can express it in terms of some of these content spaces.

>> That is true and in some ways these clusters are post processed versions of

those latent factors.

So we did a clustering on those factors and then this is somewhat interpretable,

but you're right that the dimensions of those latent factors are still difficult

to interpret.

>> So one last question, where is this type of technology in

terms of the continuum from an idea in the research lab,

out to everybody uses it in all of their systems today?

>> So my sense is, and this is more coming from academia is that

this has been explored quite extensively and there's lots of ideas out there.

I think one challenge in adopting this fully Is that,

first of all, as you throw in more types of entities.

The data sparsity problem is exacerbated.

You have just many more of these large sparse matrices.

And then the scale of the problem increases.

So you have to sort of come up with tractable algorithms which scale to

the right things.

>> So I think that's where things are,

people are navigating their way through these, is my understanding.

>> So it sounds like it's a technology to keep an eye on.

>> Yeah.

>> And it wouldn't be surprising if we start seeing some of the industry

leaders jumping into these ideas,

to try to ratchet their recommenders up a little notch.

>> Yeah, I think so.

>> Well, wonderful.

Thank you so much for joining us.

>> Thanks for having me.

>> And we'll see you again soon.