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Dec 12th 2016

Nearest Neighbor Collaborative Filtering (Coursera)

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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.


Course 2 of 5 in the Recommender Systems Specialization.


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 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.

User-User Collaborative Filtering Recommenders Part 1


WEEK 2

User-User Collaborative Filtering Recommenders Part 2

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


WEEK 4

Item-Item Collaborative Filtering Recommenders Part 2

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

Graded: Item-Based and Advanced Collaborative Filtering Topics Quiz