User-User Collaborative Filtering
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Reading list
We've selected 17 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
User-User Collaborative Filtering.
This textbook provides a comprehensive overview of recommender systems, including user-user collaborative filtering, item-item collaborative filtering, and matrix factorization.
Covers machine learning techniques for recommender systems, including user-user collaborative filtering. It provides a practical guide to building and deploying recommender systems.
Provides a comprehensive overview of machine learning, including a chapter on collaborative filtering. It is written by Andrew Ng, one of the leading experts in machine learning.
Provides a comprehensive overview of pattern recognition and machine learning, including a chapter on collaborative filtering. It is written by one of the leading experts in pattern recognition and machine learning.
Provides a comprehensive overview of machine learning, including a chapter on collaborative filtering. It is written by two of the leading experts in machine learning.
Provides a comprehensive overview of machine learning, including a chapter on collaborative filtering. It is written by one of the leading experts in machine learning.
Focuses specifically on user-user collaborative filtering algorithms, providing a detailed overview of the different approaches and their strengths and weaknesses.
Provides a practical guide to building and deploying recommender systems using collaborative filtering algorithms.
Covers deep learning techniques for recommender systems, including user-user collaborative filtering. It provides a comprehensive overview of the state-of-the-art in deep learning for recommender systems.
Provides a comprehensive overview of machine learning algorithms, including collaborative filtering. It is written in a clear and concise style, making it accessible to readers with little or no prior knowledge of machine learning.
Provides a comprehensive overview of information retrieval, including a chapter on collaborative filtering. It is written by two of the leading experts in information retrieval.
Provides a comprehensive overview of natural language processing, including a chapter on collaborative filtering. It is written by three of the leading experts in natural language processing.
Provides a comprehensive overview of AI algorithms, data structures, and idioms in Python, including a chapter on collaborative filtering. It is written by one of the leading experts in AI.
This textbook covers a wide range of data mining topics, including collaborative filtering, clustering, classification, and association rule mining.
Provides a comprehensive overview of the foundations of recommender systems, including user-user collaborative filtering. It covers the latest research and developments in the field, and provides practical guidance on how to build and evaluate recommender systems.
This textbook provides a comprehensive overview of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a hands-on introduction to machine learning, including a chapter on collaborative filtering. It is written by two of the leading experts in machine learning.
For more information about how these books relate to this course, visit:
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