Netflix Recommendation System

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Networks Illustrated: Principles without Calculus

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Princeton University

Networks Illustrated: Principles without Calculus

39 ratings

What makes WiFi faster at home than at a coffee shop? How does Google order its search results from the trillions of webpages on the Internet? Why does Verizon charge $15 for every GB of data we use? Is it really true that we are connected in six social steps or less? These are just a few of the many intriguing questions we can ask about the social and technical networks that form integral parts of our daily lives. This course is about exploring the answers, using a language that anyone can understand. We will focus on fundamental principles like “sharing is hard”, “crowds are wise”, and “network of networks” that have guided the design and sustainability of today’s networks, and summarize the theories behind everything from the social connections we make on platforms like Facebook to the technology upon which these websites run. Unlike other networking courses, the mathematics included here are no more complicated than adding and multiplying numbers. While mathematical details are necessary to fully specify the algorithms and systems we investigate, they are not required to understand the main ideas. We use illustrations, analogies, and anecdotes about networks as pedagogical tools in lieu of detailed equations. Please note that per Princeton University policy, no certificates, credentials or reports are awarded in connection with this course.

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Movie Recommendation on Netflix

One of the perks of having a Netflix subscription is getting recommendations of movies to watch. Behind the scenes, Netflix uses powerful algorithms to determine which will be suggested to each person specifically. In this lesson, we will take a look at the main ideas behind these algorithms.

Netflix Timeline5:21

Video Streaming5:19

Recommendation is Everywhere3:24

Netflix Recommendation System5:20

Netflix Prize: Logistics7:38

Netflix Prize: The Competition4:33

Our Example3:48

Raw Average7:13

User-movie Interactions7:00

Baseline Predictor6:57

Cosine Similarity9:18

Similarity Values10:58

Leveraging Similarity8:30

Neighborhood Predictor4:44

Performance of Different Methods3:30

Copy of Cosine Similarity9:18

Conheça os instrutores

Christopher Brinton
Lecturer
Electrical Engineering
Mung Chiang
Professor
Electrical Engineering

So now, let's talk about the Netflix recommendation system.

Alright, so the idea is that we have some predictor.

Just this big black box right now, okay? The predictor takes some input, which is

what we know. And generates an output, which are the

predictions. And there was predictions for what we

think that you'd like. Alright, so the predictor's going to say

oh you know this this movie I think you're going to rate it as a four star or

something like that. So for Netflix the input to the

recommendation system is each rating. Now the ratings are, are composed of a

few different metrics which are useful to us, a few different data points.

first one is the user ID, so who is the person.

This is Bob, this is Alice, Charlie, whoever.

then the movie title. Alright, so it's, so is this Good Will

Hunting, is this is A Beautiful Mind, is this Lion King whatever movie it is, what

were the number of stars. So, how many star ratings did you give

right. So it has who you are, it has title of

the movie and it has the rating you just gave and in terms of 1 to 5 you either

rate it a 1, 2, 3, 4 or 5. And also the data of a rating is

important too. And Netflix does use that but we're not

going to look at that here but data rating is also an input there.

And the output is first a set of predicted ratings.

Okay, so we don't know, you haven't watched this movie but we're predicting

you would rate it as a four star or a 3.8 stars, or something like that.

It doesn't' have to be a an integer number even though when you have the

input it's always integers, the output won't always be an integer.

the reason it won't always be an integer is because you won't actually see those

predicted ratings. As the user what you see is the

recommendation. So this is what's actually transparent to

the users, the recommendation. we think you should watch movie X, movie

Y. You watched Good Will Hunting.

We think you should watch A Beautiful Mind, or something along those source.

So they, they don't show you the predicted ratings.

Then they show you recommendations. So, this is not shown to you, this is

shown to you. And this is just showing it graphically

though and we do actually need to deal more so with the predictive ratings here.

we can't deal with the recommendations. So, we have to just evaluate the

predictor for accuracy. And we'll talk about that in a second.

Let's first just look at this table just to get an idea.

So, on this side we have the input to the system.

It's we've given it a tabular form. There is shown as a table.

Down the rows are users, and across the columns are movies.

Right, so this user and this movie, you can see this has a question mark here,

something that we don't know. You can just read.

Then we'll say maybe this user and what about this movie, right.

So you can come over through and see that he rated this movie a four star.

So on. It's just a, a concise way of depicting

that input. And the question marks, again, to note

the ones that we don't know. So, we don't know what this user rated

this movie. We don't know what this user rated this

movie. Because it's, it hasn't been inputted

yet. It's not that it's there, and we just

don't know it. But we don't know what this user thinks

of this movie, 'cuz he hasn't watched it yet, for instance.

Then. And then make a prediction and the output

again is the user movie table. We can look at it as such at least.

But it's just the the predictions, right, so the predicted ratings.

We predict 3.1 for this movie. We predict 3.2 for this one that has no

watch, 2.8, 4.5, 3.8, 3.2. And then we can use this data to say then

for each user what we think the person should watch.

So well, for this, for this user up here, for instance we see that there's two

movies, 3.2 rating on this one, 3.8 rating so we would say okay, well you

should watch this movie, for instance. Okay.

then this user down here, 2.8, 3.2, we'd say okay, well you should watch this

movie for instance. Then for the first and last one it's

trivial because there's no other movies. But.

in general there's just a ton of movies, a ton of users.

This again, is just a really small depiction of the idea.

And we can, we don't have to write, recommend just one movie, we can

recommend multiple ones. We could recommend the top 10 movies,

say, a person hasn't watched yet. Then we need to actually evaluate this

system. Okay, and again, we can evaluate it in

terms of the. Actual recommendation because then we

have to ask people, you know, what they think of the recommendations.

That we try to have a quantifiable metric.

And, the metric that we use is called the, Root Mean Squared Error.

RMSE. And it's a mouth full.

We'll go through how you actually compute it, and we'll walk through it step by

step. but it's just basically an error metric,

the lower the better. It's the difference the error, in the

prediction versus the actual values. Right?

So, really what we'll end up doing is, we will withhold some of the values that we

actually know. Like for instance, this five right here,

we might hide that from our system. We might hide this three from our system,

and this three from our system and then run through with the values that we are

assuming we know, and then test and see how close the values we hid are from what

they really are. And then we can, therefore make a

inference if that's a really low RMSE, if it's really good at predicting a few of

the values. Then for the ones that we actually don't

know we will assume that it's probably a pretty good system, right because we are

not using any of the values of this kind of same ideas that we don't know them,

the recommended, recommended system really know about this value, this value

and this value. And so that we could take this this and

this and looking at this table right here, we'd predict values here, here and

here, and then to the RSME and see how good the RSME is.

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