![]() ![]() Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. 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. The chapters of this book are organized into three categories: This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. 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. ![]()
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