This book comprehensively covers the topic of recommender systems,
which provide personalized recommendations of products or services
to users based on their previous searches or purchases. Recommender
system methods have been adapted to diverse applications including
query log mining, social networking, news recommendations, and
computational advertising. This book synthesizes both fundamental
and advanced topics of a research area that has now reached
maturity. The chapters of this book are organized into three
categories: Algorithms and evaluation: These chapters discuss the
fundamental algorithms in recommender systems, including
collaborative filtering methods, content-based methods,
knowledge-based methods, ensemble-based methods, and evaluation.
Recommendations in specific domains and contexts: the context of a
recommendation can be viewed as important side information that
affects the recommendation goals. Different types of context such
as temporal data, spatial data, social data, tagging data, and
trustworthiness are explored. Advanced topics and applications:
Various robustness aspects of recommender systems, such as shilling
systems, attack models, and their defenses are discussed. In
addition, recent topics, such as learning to rank, multi-armed
bandits, group systems, multi-criteria systems, and active learning
systems, are introduced together with applications. Although this
book primarily serves as a textbook, it will also appeal to
industrial practitioners and researchers due to its focus on
applications and references. Numerous examples and exercises have
been provided, and a solution manual is available for instructors.
General
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