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Nearest Neighbor Collaborative Filtering

University of Minnesota via Coursera

2 Reviews 54 students interested
  • Provider Coursera
  • Subject Artificial Intelligence
  • Cost Free Online Course (Audit)
  • Session Upcoming
  • Language English
  • Certificate Paid Certificate Available
  • Start Date
  • Duration 4 weeks long
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Overview

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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.

Syllabus

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


User-User Collaborative Filtering Recommenders Part 2


Item-Item Collaborative Filtering Recommenders Part 1


Item-Item Collaborative Filtering Recommenders Part 2


Advanced Collaborative Filtering Topics


Taught by

Joseph A Konstan and Michael D. Ekstrand

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