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.
- 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 ...
- Week 1 - User-User Collaborative Filtering Recommenders Part 1
- Week 2 - User-User Collaborative Filtering Recommenders Part 2
- Week 3 - Item-Item Collaborative Filtering Recommenders Part 1
- Week 4 - Item-Item Collaborative Filtering Recommenders Part 2
- Week 4 - Advanced Collaborative Filtering Topics
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
Michael D. Ekstrand
Dept. of Computer Science, Boise State University