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.
This course is part of the Recommender Systems Specialization
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Syllabus - What you will learn from this course
Preface
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
Reviews
- 5 stars53.79%
- 4 stars29.04%
- 3 stars11.55%
- 2 stars2.64%
- 1 star2.97%
TOP REVIEWS FROM NEAREST NEIGHBOR COLLABORATIVE FILTERING
Very good course, there is a glaring error in Week 4s assignment. But if you check the forums it can be easily solved
a great class, I learned some insight in these algorithms
Very good content ! Very interesting interviews with expert in the field that shows real examples. However the exercise needs a bit more work to be very useful.
Thank you so very much to open my eye see more view of recommendation field not only algorithms but use case and many trouble-shooting in worldwide business, moreover interview with noble professor.
About the Recommender Systems Specialization

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