Course Introduction

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Del curso dictado por University of Minnesota

Nearest Neighbor Collaborative Filtering

184 calificaciones

University of Minnesota

Nearest Neighbor Collaborative Filtering

184 calificaciones

Curso 2 de 5 en SpecializationRecommender Systems

In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.

De la lección

Preface

Note that this course is structured into two-week chunks. The first chunk focuses on User-User Collaborative Filtering; the second chunk on Item-Item Collaborative Filtering. Each chunk has most of the lectures in the first week, and assignments/quizzes and advanced topics in the second week. We encourage learners to treat each two-week chunk as one unit, starting the assignments as soon as they feel they have learned enough to get going.

Course Introduction3:20

Conoce a los instructores

Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
Michael D. Ekstrand
Assistant Professor
Dept. of Computer Science, Boise State University

Welcome back.

In this course, we're going to move away from

the simple popularity and the content based approaches to look

at a version of recommendation called collaborative filtering.

In collaborative filtering, we ignore the user and item attributes.

We don't care what's in the item.

We don't care who the user is.

We only look at the interactions between users and items.

And we mine patterns from these,

such as looking at what people like you also bought.

This has two significant benefits.

One, it allows us to develop algorithms that operate independently of

the characteristics of the particular items being recommended.

It also allows us to mine interesting pattern, such as other users who also

bought all of this Red Sox paraphernalia, really like the clam chowder.

>> So the key concepts in this course, first,

nearest neighbor collaborative filtering.

As a concept, as you've just heard,

it's based on the idea of finding a set of people like you, and

understanding what it is that that set of people like you also like.

We'll start with the user-user collaborative filtering algorithm,

an algorithm that directly forms neighborhoods of other people.

And we'll explore topics such as the formation of neighborhoods and the tuning

parameters that make this algorithm work when it works particularly well.

We'll also look at alternatives for

historic agreement as ways of building neighborhoods,

things like building neighborhoods based on social relationship or trust.

We'll then transition into item-item collaborative filtering.

Where we pivot things around and look at relationships among items

defined by the users who have experience with each of the items.

We'll deal with issues such as unary data.

What happens when we only know about likes, but not dislikes?

We will look at hybrids and

extensions that can bring new information into item-item collaborative filtering.

And we'll be talking about the practical implications of item-item collaborative

filtering, including how this algorithm really got its start in commercial use.

Finally, we'll pull together a bunch of advance topics, including the challenges

of getting started with a recommend your system when you don't have much data yet.

Recommending for groups.

Explaining recommendations, and threats and

attacks when people might try to undermine a recommender system.

>> This course is structured around these algorithms,

with the first two weeks spent on user-user collaborative filtering.

Where we'll have an assignment to do basic user-user collaborative filtering

in a spreadsheet, an Honors Assignment for those of you taking that track, where

you'll implement user-user CF in LensKit, and then a quiz over the concepts.

Weeks three and four will be similarly structured,

looking at item-item collaborative filtering and the advanced topics.

Again, with an assignment, a programming assignment and

a quiz to test your general knowledge of the subject.

>> So, let's go on into the course.

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