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.
Об этом курсе
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Лучшие отзывы о курсе NEAREST NEIGHBOR COLLABORATIVE FILTERING
a great class, I learned some insight in these algorithms
Very satisfied to do this, the videos are too long, very good quality and a lot of practical information. I love it!
Loved it...many thanks Prof. Joe for bringing this content to Coursera
Provides a good overview of item based and user based collaborative filtering approaches.
Специализация Рекомендательные системы: общие сведения
A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.
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