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...Show More
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. Show Less
Includes exercises and assignments, with instructor access to a solutions manual
Illustrations throughout aid in comprehension?
Provides?many examples to simplify exposition and facilitate in learningDestined to be the standard textbook in a mature field
Neighborhood-Based Collaborative Filtering
Model-Based Collaborative Filtering
Content-Based Recommender Systems
Knowledge-Based Recommender Systems
Ensemble-Based and Hybrid Recommender Systems
Evaluating Recommender Systems
Context-Sensitive Recommender Systems
Time- and Location-Sensitive Recommender Systems
Structural Recommendations in Networks
Social and Trust-Centric Recommender Systems
Attack-Resistant Recommender Systems
Advanced Topics in Recommender Systems.
“Charu Aggarwal, a well-known, reputable IBM researcher, has taken the time to distill the advances in the design of recommender systems since the advent of the web … . Extensive bibliographic notes at the end of each chapter and more than 700 references in the book bibliography make this monograph an excellent resource for both practitioners and researchers. … Without a doubt, this is an excellent addition to my bookshelf!” (Fernando Berzal, Computing Reviews, February, 2017)
ABOUT THE AUTHOR
- Business Analytics Richard Vidgen, Sam Kirshner, Felix Tan
- Artificial Intelligence Rob Callan
- Fundamentals of Operating Systems Bob Eager, Andrew Lister
- Mastering 'C' Programming Arthur Chapman
- From Data Structures to Patterns Darrel Ince
- The B-Method Steve Schneider
- Formal Object Oriented Specification Using Object-Z Roger Duke, Gordon Rose
- Computer Vision and Image Processing Tim Morris
- Infosense K. Devlin
- Multimedia Web Programming Adrian Moore
- How to Program Using Java Tony Jenkins, Graham Hardman
- Managing Information System Security Maggie Nicol