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.
