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From the course by Princeton University

Networks Illustrated: Principles without Calculus

40 ratings

Princeton University

Networks Illustrated: Principles without Calculus

40 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.

From the lesson

Product Rating on Amazon

The decision of whether or not to purchase something online is often driven by the ratings that previous customers have left for it. In this lesson, we will take a look at Amazon's review system, and the see how "crowds are wise" is another important networking principle.

Amazon & eCommerce4:28

Average Ratings10:21

The Wisdom of Crowds10:03

Rating Aggregation Challenges4:11

Naive Averaging5:57

Bayesian Ranking: Part I5:32

Bayesian Ranking: Part II9:14

Bayesian Ranking in Practice5:41

What does Amazon do? Part I5:00

What does Amazon do? Part II5:29

Meet the Instructors

Christopher Brinton
Lecturer
Electrical Engineering
Mung Chiang
Professor
Electrical Engineering

Let's briefly summarize what we've covered in this lecture.

We've talked about online product sites.

We've motivated this by discussing eCommerce, and

specifically Amazon being the dominant eCommerce company today.

We've talked about how these sites can rank product lists

and which types of rankings may be more efficient than others.

Which also led us and gave us insights into when we might

be able to trust the rankings, or the average ratings on the sites.

We also saw a lot of the wisdom of crowds and we,

we motivated that with a famous example from a Galton experiment in 1907.

When a bunch of people were trying to estimate the weight of an ox,

right, to guess the true value, in that case being the weight of the ox.

And since certain conditions were satisfied, meaning

the task was easy, there were independent,

unbiased, and also the number of people was large enough that the, the error

in each of the average guesses, or the error in the average was decreasing

as the number of people increased because they tended to average each other out.

In it's, all the errors averaged each

other out and concentrate around the true value.

It talks about rating and ranking them.

Wow, two approaches to this.

First is just the naive average, which basically says we just add up the total

number of stars in the case of Amazon, and divide by the total number of reviews.

Then we talked about a Bayesian adjusted ranking.

Which which extends the idea of a naive average, to also

take into account information given by the population as a whole.

And we, we explain that using sliding ruler concept.

So, if this is the individual, and this is the overall average, individual

average and overall average, the adjusted

average is going to be somewhere in between.

And, as the number of individual ratings

increases this is going to approach the naive

average of the individual but as it

decreases, it's going to approach the overall average.

So, as we have less information, we're going to sort

of rely on the overall average as a backup.

Then we also turned our attention to Amazon's secret formula

which is not known to the public, but we, we

tried to apply some of the techniques that we had

discussed to see if we could reverse engineer the secret equation.

And what we applied was first a Bayesian

adjusted ranking, and then we tried to look

at specific cases which would, which involved things

like review quality, reviewer quality and timing of reviews.

The main themes that we covered here, first is that

crowds are wise, which is the over arching main idea.

That we can, in certain circumstances, we can trust the opinions of the

masses to actually come up with a good estimate for some, something, some value.

The second was opinion aggregation, which just extends that idea to a more general

setting of being able to aggregate the opinions of others such as in voting.

And the third maintain that is much more general than this lecture,

and this whole course, in a sense, is the, the gap between theory

and practice, which is especially prominent

in this case, because we saw that

a lot of the techniques that we discussed aren't exactly applied in practice.

And there's lots of modifications that are made in each specific

case to account for factors that are very hard to model theoretically.

[BLANK_AUDIO]

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