Recommender Systems

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This book offers a comprehensive exploration of recommender systems, which deliver personalized suggestions for products or services based on users' past interactions. The methods discussed have been adapted for various applications, including query log mining, social networking, news recommendations, and computational advertising. The content is organized into three main categories: 1. **Algorithms and Evaluation**: This section covers fundamental algorithms, including collaborative filtering, content-based, knowledge-based, and ensemble methods, along with evaluation techniques. 2. **Recommendations in Specific Domains and Contexts**: Here, the importance of contextual information—such as temporal, spatial, social, tagging data, and trustworthiness—is examined, highlighting how it influences recommendation goals. 3. **Advanced Topics and Applications**: This part delves into robustness issues like shilling systems, attack models, and defenses. It also introduces contemporary topics such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning, alongside their applications. While primarily a textbook, the book also caters to industrial practitioners and researchers with its application focus and extensive references. It includes numerous examples and exercises, and a solution manual is available for instructors.