About 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.

Created by: University of Minnesota

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

Basic Info	Course 2 of 5 in the Recommender Systems Specialization.
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	4.2 stars Average User Rating 4.2See what learners said

Syllabus

WEEK 1

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 ...

1 video, 1 reading

User-User Collaborative Filtering Recommenders Part 1

5 videos

WEEK 2

User-User Collaborative Filtering Recommenders Part 2

2 videos, 2 readings

Graded: User-User CF Answer Sheet

Graded: User-User Collaborative Filtering Quiz

Graded: User-User CF Programming Assignment

WEEK 3

Item-Item Collaborative Filtering Recommenders Part 1

6 videos

WEEK 4

Item-Item Collaborative Filtering Recommenders Part 2

2 videos, 2 readings

Graded: Item Based Assignment Part l

Graded: Item Based Assignment Part II

Graded: Item Based Assignment Part III

Graded: Item Based Assignment Part IV

Graded: Item-Item CF Programming Assignment

Advanced Collaborative Filtering Topics

5 videos

Graded: Item-Based and Advanced Collaborative Filtering Topics Quiz

FAQs

How It Works

Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

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Creators

University of Minnesota

The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.

Pricing

	Audit	Purchase Course
Access to course materials	Available	Available
Access to graded materials	Not available	Available
Receive a final grade	Not available	Available
Earn a shareable Course Certificate	Not available	Available

Ratings and Reviews

Rated 4.2 out of 5 of 58 ratings

Awesome as always, Joe and Michael rock. The interview with Brad Miller was stellar, felt like listening to the legends of rock-n-roll!

Loved it...many thanks Prof. Joe for bringing this content to Coursera

a great class, I learned some insight in these algorithms

Very good course, there is a glaring error in Week 4s assignment. But if you check the forums it can be easily solved

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Nearest Neighbor Collaborative Filtering

Nearest Neighbor Collaborative Filtering

Nearest Neighbor Collaborative Filtering