What does Amazon do? Part I

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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

At this point, you're probably wonder what

Amazon specifically does to rank its product list.

Amazon actually follows some secret formula that combines

at least four elements that we can identify here.

First is the average rating, which we've seen used many times in this lecture.

Second is the Bayesian adjustment by review population.

So, for some comparable family of products, however they define that.

Some Bayesian adjustment being applied on top of that average rating.

Third is the recency of the review.

So how recently was the review made?

And fourth is the reputation score of the reviewer.

So this is a quality measure to see how good that

review or how truthful, meaningful that review likely is to be.

The exact formula is not known outside of Amazon.

But we may be able to reverse engineer

how these rankings are generated through an example.

So let's consider the top 20 Thermoses from January 2013.

And in order to do that, we're going to write a search

query of Thermos and go to Home & kitchen > Kitchen & Dining.

It's just a sequence of filters that you enter onto Amazon.

And then at the time the top 20 Thermoses in

January 2013, showed this when you combine all the results.

Here's the Thermos number here of, one through 20.

This is the number of reviews for each of the Thermoses.

So the first one had 210, for instance.

Then the number of five star, four star,

three star, two star, and one star reviews.

Then this average review, which is shown on

Amazon itself, and then the average rating, which we

can compute just by taking the number of

stars and dividing by the total number of reviews.

So from this list we can quickly extract two rank-ordered lists.

First is the ranking by Amazon, which is just the way that list

is sorted, one, two, three, four, five, six, all the way through 20.

Second is the ranking by average rating.

Which is, in the case of Amazon, rounded to an average customer review.

So, this is the ordering, we're not going to do this computation here,

but feel free to take the table and do the Bayesian adjustment computation.

And so this order, 17 followed by 15 followed by one two four

three, which basically means that in this order, the 17 would go first.

Then 15, then one, then two, then four, and so forth.

So you can see that there's quite a bit of difference between the way that

Amazon's ranking this from one to twenty, and

how it would've been ranked by average rating.

So the ranking based on average rating has

many differences from Amazon's ranking, as we can see.

For instance, Amazon's placing items 17, 15, 18

and 19 much lower than the averages would indicate.

So, if you look at item 17, for instance, item 17

actually has the highest average rating in this whole list, if

you look at it, and it's placed all the way down

at 17 out of 20 and similarly with 15, 18 and 19.

They have higher ratings than those products around them at least.

And so we would expect them to be ranked

higher if we were just looking at the average rating.

Second is that Amazon places 15 or.

Second is that Amazon places five, eight, 11,

14, much higher than their averages would indicate.

So if we look at, five, for instance, this is placed much higher than

what we would expect because it's actually one of the lowest ratings on this list.

So let's try and reverse engineer what

factors might have contributed to the actual ranking.

From our discussion already, the following techniques may help.

First is Bayesian adjustment.

The population size of the ratings matter.

The raw rating scores must somehow be weighted with the population size.

Second is the recency of the reviews.

You can imagine that perhaps some of the

reviewers rated their Thermoses as soon as they purchased

them, and gave them high ratings because the products

appeared durable, though sometimes items deteriorate quicker than anticipated.

So, some people who had the products initially may

have rated them high, and then maybe they saw

that they deteriorated quickly, so then people who had

waited longer and reviewed them, gave them worse ratings.

Third is the quality of the reviewers or reviews.

There's four categories of this.

One is the reputation score of the reviewer, reviewers of high reputations we

might think should be given more say than the average customer review for product.

Second is, the quality of the review, which

we can measure, maybe in terms of the length.

Or, whether it contains certain keywords within the

review and the text reports of the review.

Third is the review of the review.

Higher review scores indicate that, customers

found a review helpful and accurate.

So, if we have that 150 people out of 160 people found it helpful.

That's a very high fraction of people who upvoted rather than downvoted it.

And fourth might be the timing of the

reviews, specifically in terms of, detecting, [SOUND] reviewer spam.

Detecting review spam.

So if we look at, Some intricate details with the timing of different reviews and

how often they come in, whether we have a slew of negative reviews at some point.

It could be from competitive products, for instance.

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