Nearest Neighbor Collaborative Filtering

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Nearest Neighbor Collaborative Filtering

This course is part of Recommender Systems Specialization

Instructors: Joseph A Konstan

15,418 already enrolled

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6 modules

Gain insight into a topic and learn the fundamentals.

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 assignments

Taught in English

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This course is part of the Recommender Systems Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 6 modules in this course

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.

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.

What's included

1 video1 reading

What's included

5 videos

What's included

2 videos2 readings2 assignments1 programming assignment

2 videosTotal 13 minutes

Assignment Introduction9 minutes
Programming Assignment - Programming User-User Collaborative Filtering5 minutes

2 readingsTotal 20 minutes

Assignment Instructions: User-User CF10 minutes
Introducing User-User CF Programming Assignment10 minutes

2 assignmentsTotal 50 minutes

User-User CF Answer Sheet30 minutes
User-User Collaborative Filtering Quiz20 minutes

1 programming assignmentTotal 180 minutes

User-User CF Programming Assignment180 minutes

What's included

6 videos

What's included

2 videos2 readings4 assignments1 programming assignment

2 videosTotal 10 minutes

Item-Based CF Assignment Intro Video5 minutes
Programming Assignment - Programming Item-Item Collaborative Filtering5 minutes

2 readingsTotal 20 minutes

Item-Based CF Assignment Instructions10 minutes
Introducing Item-Item CF Programming Assignment10 minutes

4 assignmentsTotal 40 minutes

Item Based Assignment Part l10 minutes
Item Based Assignment Part II10 minutes
Item Based Assignment Part III10 minutes
Item Based Assignment Part IV10 minutes

1 programming assignmentTotal 180 minutes

Item-Item CF Programming Assignment180 minutes

What's included

5 videos1 assignment

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Instructors

Joseph A Konstan

University of Minnesota

11 Courses221,538 learners

Michael D. Ekstrand

University of Minnesota

6 Courses112,556 learners

Offered by

University of Minnesota

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