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En provenance du cours de Princeton University

Networks Illustrated: Principles without Calculus

43 notes

Princeton University

Networks Illustrated: Principles without Calculus

43 notes

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. All the features of this course are available for free. It does not offer a certificate upon completion.

À partir de la leçon

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

Rencontrer les enseignants

Christopher Brinton
Lecturer
Electrical Engineering
Mung Chiang
Professor
Electrical Engineering

And so we've definitely covered a lot in this lecture.

probably a lot more than we do in a typical lecture.

Especially mathematically. And again, this will be the last, lecture

where we really do, any s-, semi complicated math at all.

This isn't really even complicated math. Just, more complicated than what you

might want to see. And so we, we won't be doing that in the

future, but it just was really kind of necessary to get the key ideas across.

but now so, Netflix we looked at Netflix obviously throughout here, and the first

thing we looked at was internet video streaming.

You know what that means to be on your device and to be, streaming from the the

internet wherever it is on the internet. And that was first thing we looked at.

We talked a little bit about that. We looked at movie recommendation.

how Netflix, how all your actions that you do on the Netflix site when you

either rate a movie, tell them you're not interested or add to a queue, how that

effects the movies that get recommended to you.

we looked at the Netflix Prize. We looked at the timeline of that and the

major events over that 2 year period. which is really ignited the research

community for awhile in these fields and was very interesting.

then we really just looked at recommendation specifically of the

Netflix problem. We looked at prediction, right how it's

important. You need to make a prediction of what you

think the users are going to rate the movies based upon what you know about

them, and maybe what you know about people or movies who are similar to what

you're looking at. we looked at two methods.

first is the baseline predictor where we just looked at rows and columns in

isolation from one another. apart from the raw average mean, which is

really taking everything into account but it's not really doing any sort of

similarity. And then we looked at the neighborhood

method which uh,uh, is an incremental upon the baseline predictor, at least in

the way we presented it, and then actually doesn't treat rows and columns.

So with the Baseline predictor what we're doing it's looking at any one of these

that are given time slice out like that. And with the neighborhood method, then

we're looking across columns in the case that we're doing movies neighbors, so

we'll be looking across rows, in the case that we were doing.

users as neighbors to one another. We looked at the RMSE, right?

That acronym Root Mean Square Error. And that's that's a standard approximate

used to evaluate recommendation system. So then that's necessary here because you

can't necessarily always rely on users to tell you whether they like the movie or

not. You need some way to quantify it and the

RMSE is very standard way of doing that, beyond the recommendation methods.

And, so, 2 major themes. First of all is the idea of collaborative

filtering, right, so it's exploiting similarities across the table.

It's actually, make predictions that way. exploiting the certain things you might

see, certain structure in that. And the second was, reverse engineering

which we talked about. the fact, that in this case, we don't, we

want to avoid reverse engineering, we want to avoid, having someone build the

end results without building more general recommendation system first.

An like the idea we said before, it's like getting the test before hand and

then taking that test when you walk in. Rather than studying for the test so you

learn everything and then being tested on a certain portion of it.

And so those are the main themes. Alright, well hope you enjoyed the

lecture and I'll talk to you next time.

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