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. This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics. The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit. By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project.
WHAT YOU WILL LEARN
Coursera
Dive into the world of personalized recommendations with our 'Introduction to Recommender Systems' course. Explore non-personalized and content-based filtering methods, essential for creating effective recommendation systems that improve user experiences across various platforms.
Coursera
Dive into the world of Recommender Systems with this Coursera course designed to teach you the evaluation techniques and metrics crucial for optimizing recommendation algorithms. From measuring prediction accuracy to assessing rank accuracy and decision-support, this course will equip you with a deep understanding of how different factors like diversity and serendipity impact user experience and business success.
Coursera
Dive into the world of personalized recommendation systems with our Nearest Neighbor Collaborative Filtering course. Master the fundamentals of making customized suggestions by understanding how algorithms identify and combine ratings from users with similar preferences. Perfect for data enthusiasts and professionals aiming to enhance user experience through targeted recommendations.
Coursera
Explore a comprehensive course on Coursera that delves into the intricacies of Matrix Factorization and Advanced Techniques. Learn how to construct powerful recommender systems by mastering dimensionality reduction and combining various algorithms effectively.