May 1, 2024
4 minute read
Recommendation systems are powerful tools that are used to discover what users may like or be interested in based on analysis of previous interactions. They are used by a wide variety of companies and organizations to recommend products, services, music, articles, news, or anything else that can be recommended.
Why Learn Recommendation Systems?
There are many reasons why someone might want to learn about recommendation systems. Some of the most common reasons include:
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Find a path to becoming a Recommendation. Learn more at:
OpenCourser.com/topic/q4lytz/recommendatio
Reading list
We've selected nine 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.
Covers the algorithmic foundations and practical applications of recommender systems, with a focus on collaborative filtering and matrix factorization.
Explores advanced topics in recommender systems, including contextual recommendations, personalized ranking, and multi-criteria decision making.
This textbook covers the fundamental concepts and algorithms used in recommender systems, with a focus on scalability and efficiency.
Contains a chapter on recommender systems, providing a general overview of the topic and covering popular algorithms and techniques.
Focuses on the use of social tags in recommender systems. It provides a comprehensive overview of the topic, covering various techniques and applications.
Introduces deep learning techniques for recommender systems. It covers various deep learning models and their applications in recommendation scenarios.
Includes a chapter on recommender systems in data streams, focusing on techniques for handling evolving user preferences and data.
Covers the use of recommender systems in social media applications, including friend recommendations, group recommendations, and personalized content discovery.
Ce livre présente les concepts fondamentaux des systèmes de recommandation et les algorithmes qui les sous-tendent, ainsi que des exemples d'applications concrètes. Il est rédigé en français.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/q4lytz/recommendatio
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