This book comprehensively covers all the topics of cutting-edge and emerging recommender systems that provide personalized recommendations of items or services based on past behavior.

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 social networking, movie recommendation, query log mining, news recommendations, and computational advertising.

The 21 chapters are placed in 5 broad sections looking at Introduction to Recommender Systems; Machine Learning-Based Recommender Systems; Content-Based Recommender Systems; Blockchain & IoT-Based Recommender Systems; and Healthcare Recommender Systems. 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. Various robustness aspects of recommender systems, such as trust-based recommendation system, recommendation system on tourist, medical sciences, and the agricultural field are discussed. In addition, current topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria decision support systems, and active learning systems, are introduced together with applications.

Audience
The book will be used by engineers in information technology, artificial intelligence, human-computer interaction, machine learning and analytics specialists, as well as marketeers and web managers in industry.