This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations. After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit. In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems.
Intro to Course and Specialization
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來自 University of Minnesota 的課程
Introduction to Recommender Systems: Non-Personalized and Content-Based
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Preface
This brief module introduces the topic of recommender systems (including placing the technology in historical context) and provides an overview of the structure and coverage of the course and specialization.
與講師見面
Joseph A KonstanDistinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
Michael D. EkstrandAssistant Professor
Dept. of Computer Science, Boise State University
Welcome back!
This video is an introduction to our specialization on recommender systems and
the first course in that specialization.
Let's start by introducing ourselves.
I'm Joe Konstan, I direct the Group Lens Research Lab at the University of
Minnesota, where I'm a Professor of Computer Science and Engineering.
I've been working in the field of recommender systems since 1995 and
among other things I oversee and run the movie lens recommender system.
I'm delighted to be here with Michael Ekstrand teaching this material,
as we try to bring what we do in the lab, and what we've done with
people in industry, out into greater use in the world, Michael?
>> I'm Michael Ekstrand, an Assistant Professor of Computer Science at
Boise State University, where I've been there since the Fall of 2016.
I've been doing recommender systems things since about 2009,
the second or third year of my PhD studies.
And my research generally works on how recommender systems interact
with the needs and the experiences of the users that engage with them.
This course and specialization is dedicated in memory of John Riedl, who,
as you may remember from the previous video,
was a pioneer in some of the early recommendation systems work.
And worked very hard over 20 years to build up recommender
systems as a field both of academic research and of industrial application.
And he worked closely with many people in the field.
He was my primary adviser for several years of my PhD studies and
was the founder of the Group Lens Research Lab at the University of Minnesota.
>> Yeah, I worked closely with John for many years, he was a wonderful colleague.
He and I started the ideas behind this course.
And when he was unable to continue, I was really honored
that Michael, as one of his senior PhD students, stepped in and
worked to complete this course and bring it to fruition in its first form.
And we're just delighted to be able to do that in his memory.
So, let's start with an overview of this specialization as a whole.
It has four courses.
This first course is on non-personalized and content-based recommenders.
The second course looks at nearest-neighbor collaborative filtering
techniques.
The third course focuses on evaluation and
metrics, looking both at data centered and user centered evaluation.
And the fourth course looks at matrix factorization techniques, and
other advanced techniques and topics.
To complete this specialization, learners will complete a Capstone Project,
that is a case study analysis of designing the best recommender for
a particular business use case,
using the material covered in all four of these courses.
The courses are designed to build upon each other sequentially.
If you don't know anything about recommender systems,
we strongly encourage taking them in order.
Of course if you have a background in recommender systems, but
are trying to pick up a little bit from a topic that you may not be familiar with,
we welcome you to take any course individually.
But we do ask you to remember that because the material in the courses is designed
sequentially, some of the activities and exercises as you go forward may
depend on deep knowledge of the things that were covered in previous courses.
In particular, the programming assignments in the Honors Track,
which we'll talk about in a moment, are dependent as you go through sequentially.
And it would be difficult for somebody to take the third or
fourth course without having taken the first and second first.
So we have two tracks here.
The Standard Track does not require programming and
does not have programming activities in it.
We have found from previous instances of this material in online learning
that students with a business background, students with basics statistics,
students who want to apply this from a marketing perspective,
or who simply want to learn the material without the amount of time and
effort that's involved in building it into programs, can learn the algorithms and
how they apply equally well by going through a set of other exercises.
And we will have algorithms implemented primarily in spreadsheets and
quizzes, and other exercises to help you master and
demonstrate your mastery of recommender systems.
>> The Honors Track of this course is for
those of you who would like to extend your knowledge by actually implementing and
working with implementations of the algorithms yourself.
And in this track there are additional
programming assignments where you'll be implementing or
experimenting with different recommender algorithms using the LensKit toolkit.
And LensKit is an open-source recommendation toolkit in Java.
And to anticipate a question that will likely arise in the forums, yes, if you're
doing the programming assignment, you do need to do them in LensKit, in Java,
or it maybe possible to complete some of them in another JVM based language.
But doing this has two major benefits.
One, LensKit does a lot of heaving lifting, so
we don't have to ask you to write as much code.
And two, having everything in a standardized environment
makes it possible for us to grade them in a fashion that allows us to deliver
these kinds of assignments over the open Internet.
The Honors Track includes all the material in the Standard Track
plus an additional one to two programming assignments per course.
Also, if you're on the Honors Track, the capstone is going to have a program
evaluation component to go with the rest of the capstone experience.
These courses are going to focus on core algorithms and metrics in recommender
systems, with examples both from academic research and industrial applications.
Then we're going to have a number of assignments where you learn more
about the algorithms by actually implementing them,
either in a spreadsheet or as a computer program.
And then, by course three,
evaluating the performance of the different algorithms that you implement.
We're also going to include broad coverage of related topics.
As we mentioned in the introductory video,
algorithms are just one piece of the whole recommender package.
We have the user experience, the user psychology, we've got the data processing
and we're going to include material on all of these subjects throughout the courses.
We also have interviews with a number of leaders both from industry and
academia in the filed of recommender systems,
to give you a broad array of additional perspectives.
For the background that you need to succeed in these courses,
we will be doing a fair amount with statistics, some with matrices, etc.
So having a college level algebra background is useful.
Basic computing concepts and skills to understand the ideas of algorithms,
mathematical formulas, spreadsheet computations.
For those of you in the Standards Track we don't expect you to have programming
experience, but you should be able to find your way around Excel or Google sheets.
If you're taking the Honors Track, it is best to have some experience doing Java
programming with algorithms and data structures.
If you've taken the Introduction to Computer Science and
Data Structures Classes in the Computer Science Program, that's about the level of
experience that we think you need to have in order to succeed well in this class.
>> For those of you who maybe new to online courses like these,
we should say a word about interaction.
We're not able to take questions individually or directly.
All questions must be submitted through the class forums.
And then there's a mechanism in place to vote up the questions
that you feel are most important or most relevant.
We'll be watching the forums and particularly watching the high vote
getting questions to provide posts and answers to things that
your peers are unable to answer and that you say really need responses.
We also jump quickly through the course structure and workload.
Each course is designed to take about four weeks, five is pretty reasonable as well.
Every course has two or three assignments in the Standard Track.
There's a quiz for every major topic as a way of testing and
demonstrating your mastery.
In the Honors Track, there's an additional one to two assignments per course.
And we break the course and schedule it into weeks to help guide you.
But we want to point out right here that in many cases a topic is spread
over two weeks with the assignments listed as due at the end of the second week.
If you're to succeed with these assignments, you'll start them early.
We typically put the lecture material up front, but you will succeed more rapidly
if you're reading the assignment around the beginning of any given topic,
and start working on it as soon as you learn enough material to do so.
That's a particular issue with the Honors Track where getting yourself up and
going and programming sometimes takes a few days.
We also want to mention academic standards.
This is an academic course.
It's designed to build your skills, but it's also a representation of your work.
Coursera has an honor code online, we expect people to adhere to it.
Assignments and exams are expected to be your own work.
You are, of course, free to and encouraged to work together with others to study.
But when it becomes time to work on an assignment problem, or to take a quiz or
exam, we expect you to do that alone.
>> We're continually learning about how to run these courses.
And we'll be studying how this course book goes.
Both to make adjustments as the course proceeds, where we can, and
also to shape future courses that we may offer.
Part of this might be surveys and other opportunities to provide feedback.
And we encourage you to participate in those when they're available.
And we often partner with educational researchers to analyze data, and
learn from the experience of offering the course, from the experience of our
students, from the kinds of difficulties and challenges that they encounter.
If you're interested in seeing some of what we've learned so far,
we have a paper in the April 2015 issue of ACM Transactions on
Computer-Human Interaction.
That reports on our experience running the first version of this course, both online
and teaching a version of it in the classroom at the University of Minnesota.
The paper is also available from the URL in the slides accompanying this lecture.
So this first course is about non-personalized and
content-based recommendation.
So here you'll learn how to recommend items, products, etc.,
based on overall preference, popularity, such as restaurants
that get the highest star ratings, or products that people buy the most often.
How to segment some of those statistics by demographic characteristics to do
stereotyped recommendation, that recommend things that are popular with users
who share demographic characteristics with the user visiting your site.
As well as, how to use the content of items and
surrounding items in order to build a content based filtering recommenders that
are personalized using the terms in the descriptions of the items,
the movies, or whatever it is that you're recommending.
And to conclude, we're very glad you've chosen to take this class and
this set of classes.
We do encourage you to own your experience with these classes.
We want you to feel no stigma with just viewing the lectures.
If that's what you want to get out of them,
view the videos, learn some things, that's completely fine.
Commit enough time to get value, to get what you're looking for
from these classes.
But we want them to be available to help you get what you need,
not just to follow the program that we've set up.
