Name: 5 1 推薦系統概述 16 51 (5 1 Overview of Recommender Systems 16 51)
Uploaded: 2021-01-14T07:02:42.000Z
Duration: 16 min 52 s
Description: 【看影片學英語】數萬部 YouTube 影片，搭配英漢字典即點即查，輕鬆掌握單字發音與用法，長久累積看電影不必再看字幕。

Welcome back to Mining of Massive Datasets.

We're going to start with an overview of recommendation systems, and

Today, we are going to look at the two most common types of Recommender Systems.

Content-Based Systems and Collaborative Filtering.

And finally we're going to look at how to evaluate Recommender Systems,

Imagine any situation where a user interacts with

Now these items could be products at Amazon.

They could be music from Pandora's catalog.

Or they could be the news items on Google News.

What really matters, is that there are tens of thousands, or

hundreds of thousands, or millions of items.

And the user is interacting with this catalog.

There's two ways in which a user can interact with a large catalog of items.

The first is such, user knows what they're looking for, and they go and

they search the catalog for the precise item that they're looking for.

Now when you have a very large catalog of items,

very often the user doesn't know exactly what they're looking for.

And this is where recommendations come in.

The system recommends to the user certain items that they think the user will be

interested in, based on what they know about the user.

Now why do we really need such recommendations?

The key that made recommendations so important and

why a recommendation system developed so much in the last ten or 20 years.

If that he moved from an area scarcity to an area of abundance.

Imagine that you were out shopping 20 years ago,

you'll find a certain number of products on the shelves of the local retailer.

Now, even in the really large retailer like, like a Wal Mart, for instance.

Shelf space is a key, is a scarce commodity.

It limits the number of items that a retailer can carry.

Shelf space is expensive, because it involves real estate costs.

And, therefore, a retailer can carry only a certain number of products.

Now, a similar situation applies in the case of, for example, TV networks.

A TV network can carry only so many shows, because there's only so

many movie theaters, so they can only ser, screen a certain number of movies.

Now once the internet was developed, things changed.

The web enables zero-cost dissemination of information about products.

And what this means, is that we can have many more products than ever before.

There is no shelf space limitation on the number of products.

That's why the number of products on Amazon is much,

much more than the number of products available at any physical retailer.

The number of you know, movies available on Netflix is more than the number of

movies that have been available, available at Blockbuster and so on.

This near-zero-cost dissemination of information gives rise to

a phenomenon that's called the long tail phenomenon.

Now imagine a graph, where on X-axis we've taken the items in the catalog.

Remember, items might be books, or music, or video, or news articles.

And we've ranked these items by popularity.

So the most popular items are on the left, and

as they move towards the right, the items become less and less popular.

Well, I mean the number of times the item is purchased in a week.

Or the number of times a movie is viewed in a week, or a month, or

Now, on the Y axis, you have the actual popularity, which in this case I've

shown as the number of purchases per week, it could be number of views per week, or

it could be number of, you know, plays per month, for some music, and so on.

So in general you have items ranked by popularity along the X axis,

and the popularity itself along the Y axis.

Now when you take items you know, in a large catalog.

and you plot them on this curve you get a curve that looks like this.

You can see that the score, you know, has a very steep fall initially.

the, the, you know, you have a really, really, a few really,

And then as you move towards the right as the,

you know, as the item rank becomes greater the popularity falls off very steeply.

But at a certain point, you can see that this popularity stops, you know,

And, you know, it quite reaches the X axis.

The interesting thing here, is that there is a cut off point.

The you know, items that are less popular than this cut off point.

You know, might be purchased perhaps just once a week.

If you're a physical retailer like a Wal-Mart, it's not economic to

stock this item, because the rent cost of stocking the item is more than you make,

any right thinking retailer doesn't stock items that are unpopular.

The, you know, they only stock the, the head of the distribution.

So there's this cutoff point that I show on this graph here and items that are more

popular then this, the, the more popular items are available at a retail store.

But the less popular items, the items that are to the right of the cut off point,

Now this phenomenon applies to books, to music, to movie,

to videos to news articles for example, there are only so

many news articles in newspaper, but when you go online you can see the rest of

the news articles are less popular, news articles that are off to the right.

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5 1 推薦系統概述 16 51 (5 1 Overview of Recommender Systems 16 51)

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