Item-Item Collaborative Filtering
We're still working on our article for Item-Item Collaborative Filtering. Please check back soon for more information.
Find a path to becoming a Item-Item Collaborative Filtering. Learn more at:
OpenCourser.com/topic/lutnwt/item
Reading list
We've selected 11 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
Item-Item Collaborative Filtering.
This textbook provides a comprehensive overview of recommender systems, including item-item collaborative filtering. It is written in a clear and concise style, making it accessible to readers of all levels.
Provides a comprehensive overview of deep learning for recommender systems, including item-item collaborative filtering. It is written in a clear and concise style, making it accessible to readers of all levels.
These lecture notes provide a comprehensive overview of recommender systems, including a discussion of item-item collaborative filtering. They are written by a leading expert in the field and are a valuable resource for anyone who wants to learn more about this topic.
Covers machine learning techniques used in recommender systems, including item-item collaborative filtering. It provides practical insights and case studies on building and deploying recommender systems in various domains.
Includes a chapter dedicated to collaborative filtering and item-item collaborative filtering in particular. It provides a solid theoretical foundation and mathematical treatment of these techniques, making it suitable for readers with a strong background in data mining.
Provides a practical guide to recommender systems, including item-item collaborative filtering. It offers hands-on tutorials, code examples, and case studies to help readers implement and evaluate these techniques.
This tutorial provides a concise and accessible introduction to item-item collaborative filtering. It explains the fundamental concepts, algorithms, and evaluation methods, making it a useful resource for beginners in the field.
Provides a machine learning perspective on recommender systems, including item-item collaborative filtering.
Provides a look at recommender systems in social networks, including item-item collaborative filtering.
Provides a look at recommender systems for news, including item-item collaborative filtering.
Provides a look at recommender systems for movies, including item-item collaborative filtering.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/lutnwt/item
Save
