Introduction to Recommender Systems

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Deploying Machine Learning Models

University of California San Diego

Course 1 of 4 in the Python Data Products for Predictive Analytics Specialization

In this course we will learn about Recommender Systems (which we will study for the Capstone project), and also look at deployment issues for data products. By the end of this course, you should be able to implement a working recommender system (e.g. to predict ratings, or generate lists of related products), and you should understand the tools and techniques required to deploy such a working system on real-world, large-scale datasets. This course is the final course in the Python Data Products for Predictive Analytics Specialization, building on the previous three courses (Basic Data Processing and Visualization, Design Thinking and Predictive Analytics for Data Products, and Meaningful Predictive Modeling). At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.

Skills You'll Learn

Python Programming, Big Data Products, Recommender Systems

From the lesson

Introduction

Welcome to the first week of Deploying Machine Learning Models! We will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of recommender systems and differentiate it from other types of machine learning

Introduction to Recommender Systems4:40

Recommender Systems versus Other Forms of Supervised Learning7:33

Collaborative Filtering-Based Recommendation19:05

Taught By

Ilkay Altintas
Chief Data Science Officer
Julian McAuley
Assistant Professor

Transcript

So in this lecture, we're going to try and motivate the problem of recommender

systems as an important Data Product which we could develop later on.

And we'll describe several examples of recommender systems on the Web.

Okay, so what is recommendation and why [INAUDIBLE] do it?

Well, recommender systems are very common.

We're using them all the time whenever we get recommended

content by services on the web.

So a classic example of what a recommender system might be doing is to

help us discover new content.

And this is an example from Amazon, some recommendations of instant video for

me on the basis of a movie I have watched previously.

These movies are somehow similar or

they're movies I'm likely to rate highly if I enjoyed the previous movie.

Maybe you could try and guess what that is.

Another set of example of a recommender systems would be,

and following again from Amazon, in this case I'm watching Harry Potter 1.

And it says you might like Harry Potter 2, Harry Potter 3, Harry Potter 4,

Harry Potter 5, Harry Potter 6, Harry Potter 7 part 2,

Harry Potter 7 part 3, you get the idea.

So well, are these good or bad recommendations?

This may be not so obvious.

They're not really helping me to discover new content, rather this recommender

system would be helping me to identify content I probably already know about.

And it's just surfacing it to a more relevant or

to a more prominent part of the user interface.

It can also be useful.

So it's just helping us find or

helping us access content that we were already looking for.

Recommender systems could also hep us to discover just which things go together.

This is an example again from Amazon of people who bought x also bought y.

So if you're looking at some pair of jeans and it says you might like this shirt,

somehow think those items go together or be mutually compatible,

perhaps on the basis of people who would co-purchase both items.

A recommender system should also try and

personalize user experiences in response to user feedback is an example from

a streaming music service where you can click thumbs up or

thumbs down on a particular track or you can skip the track.

And based on what you do, the future tracks that are surfaced

should be personalized to better match your preferences.

So if you click thumbs up,

maybe you'll get more tracks that are similar to this one.

If you click thumbs down, maybe you'll get tracks that is somehow different.

Also the advertisement that's recommended here is really the operative

recommender system.

And it's somehow recommending a product that is contextually relevant to the music

you are listening to.

This is another example of a recommender system from Netflix.

In this case,

they're just trying to surface their estimate of how I would rate a movie.

I think this feature's non-interactive on Netflix.

What it was doing is trying to estimate in a personalized way what would I rate

this movie.

The important thing to notice here is this rating of 3.6 stars is not like the global

average rating from Mad Max rather it's saying, how would I rate this movie?

And that could help me to know whether this movie is good for

my personal interests.

Or it could just give me additional contextual information that says this is

how you'll rate this movie.

Maybe tonight I just happen to be in the mood for

a movie that I would rate 3.6 stars.

Okay, outside of things like e-commerce like the previous examples I was showing

which may be the most obvious.

We see recommender systems all the time, for example, in priority inbox,

which ranks emails according to whether I'm likely to respond to them.

Broadly speaking, that's an example of a recommender system.

Or in things like personalized health, if I'd like to estimate how an individual

might respond to a specific regime of treatment or

things like friend recommendation on Facebook to People You May Know feature

is really an example of a recommender system.

It's not recommending things to people, it's recommending people to other people.

So whenever we're trying to estimate this personalized reaction of an individual to

some stimulus or item or some product,

broadly speaking that's an example of recommendation technology being applied.

Okay, so there are all these different versions of recommender systems,

content discovery, building better user interfaces, finding things that go

together, personalizing experiences or just estimating what we like.

Really, recommender systems are just using machine learning to build models

of people's preferences, opinions, and behavior.

Okay, so in this lecture,

all we've really done is to describe some of the most common use cases

of recommender systems that we might observe on the web to try and

motivate the importance of this problem as a data product that's worthy of study.

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