May 1, 2024
Updated May 10, 2025
19 minute read
Recommendation systems are a specialized type of information filtering system designed to predict the "rating" or "preference" a user would give to an item. They are the engines that power the personalized experiences we've come to expect from online services, suggesting everything from movies and music to products and news articles. At a high level, these systems analyze user data – past purchases, watched videos, browsing history – to make educated guesses about what an individual might find interesting or useful in the future. The primary goal is to cut through the noise of overwhelming choice, presenting users with relevant options and making their online interactions more efficient and enjoyable.
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Find a path to becoming a Recommendation Systems. Learn more at:
OpenCourser.com/topic/ont53i/recommendation
Reading list
We've selected three books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Recommendation Systems.
Provides a machine learning perspective on recommender systems, covering topics such as collaborative filtering, content-based filtering, and hybrid approaches. It good choice for readers with a background in machine learning.
Focuses on the design and evaluation of recommender systems in social networks. It good choice for researchers and practitioners who are interested in building recommender systems for social networks.
Focuses on the use of deep learning for building recommender systems. It good choice for researchers and practitioners who want to learn about the latest advances in deep learning for recommender systems.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/ont53i/recommendation