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This book covers the topic of recommender systems comprehensively, starting with the fundamentals and then exploring the advanced topics. The chapters of this book can be organized into three categories:1. 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.2. Recommendations in specific domains and contexts: The context of arecommendation 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.3. Advanced topics and applications: Various robustness aspects ofrecommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as factorization machines, multi-armed bandits, learning to rank, group recommender systems, multi-criteria systems, and active learning systems, are discussed together with applications.Although this book is primarily written as a textbook, it is recognized that a large por- tion of the audience will comprise industrial practitioners and researchers. Therefore, the book is also designed to be useful from an applied and reference point of view. Numerous examples and exercises have been provided.