Performance of Different Methods

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來自 Princeton University 的課程

Networks Illustrated: Principles without Calculus

39 個評分

Princeton University

Networks Illustrated: Principles without Calculus

39 個評分

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.

從本節課中

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

與講師見面

Christopher Brinton
Lecturer
Electrical Engineering
Mung Chiang
Professor
Electrical Engineering

So let's lets rap up our our results here and talk a little bit about what we've

seen. first like to graph the RMSE with

different methods that we have used in a few others ones that we didn't.

so here is the RMSE in the blue is the training data, in the red is the test

data. So you can see first is that just simple

average predictor, where we had 3.5 for all the entries.

And this is the baseline predictor, so you can see the improvement in both the

training and the test data down for the baseline predictor.

So, it's a huge drop when we actually incorporated that.

and then the neighborhood method, which was one.

which is what we, we had done. And so again, you see another drop there.

And then a few other things that we didn't look at any of these other ones,

these are the three that we did. We did the simple average, then we did

the base line and the first neighborhood. but a few things that we didn't explore.

First is that we could have used users as neighbors, and I don't have a graph of

that here but it's really the same thing. We just draw similarities along the rows

instead of along the columns. and another thing is we could have used a

neighborhood method with multiple neighbors.

And, you know, there's no telling which, how many neighbors is the correct one.

One way to do it is you'd run it for two different tries and then see which one

gives you the lowest RMSE on just your training data because remember you can't

look at your test data until you're all done.

but, and then you can see which one gives you the lowest on the training data but

again there's still no guarantee that it's going to be the lowest on the test

data. but this is showing if we had used two

neighbors, actually what we would have gotten, and the math gets a little more

complicated there. but you see it's a slight improvement

over the neighborhood method with one neighbor, but actually the with the test

data it actually jumps up, a little bit. so that's really interesting.

Now another thing is that we said that we could optimize the baseline values.

Remember we just took the average in the rows and columns and subtracted from the

overall average 3.5. but really we can, we can actually solve

an optimization problem to get the baseline lower and now we're lower and if

we had done that we would have gotten 0.397.

And 0.6311, and those were lower than these values over here which is what we

compare them to, these guys. And then if we had done the opt, the

neighborhood method with these baseline values, right, we would of dropped even

lower. And then you would actually see how the

neighborhood method with two neighbors is yeah it gets, gives you lower values in

both case than the neighborhood method with just one neighbor.

And so interesting to see how different decisions along the way, whether or not

you're going to optimize, can determine whether or not things are going to be

better or worse for you. So again you just go by intuition, what

we think is going to work. And we go with that.

And so, these are, these are really, I just want to emphasize, these are just

the key ideas, I mean, there is so much more to this.

It's such a vibrant and abundant field, in machine learning and data mining.

And there's courses, on courses that are specifically on machine learning, for

instance. And those are all really interesting

fields. And you know, in a future version of this

course, maybe we'll go into more depth with some of this stuff.

but this just sort of grazes the landscape, so to speak.

And you should have the key ideas now what Netflix does to recommend movies for

their users. So they exploit baseline and neighborhood

predictors as portions of it.

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