Overview
- 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 learning
- Destined to be the standard textbook in a mature field
- Includes supplementary material: sn.pub/extras
- Request lecturer material: sn.pub/lecturer-material
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About this book
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.Similar content being viewed by others
Keywords
- Collaborative filtering
- Data mining
- Recommender systems
- Social network analysis
- Social tagging
- Graph based methods
- Personalization
- Social networks
- Machine learning
- Industrial systems
- Mobile recommender systems
- Knowledge based recommender systems
- Clustering and neighborhood-based methods
- Item-oriented and user-oriented methods
- Link prediction methods
Table of contents (13 chapters)
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Front Matter
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Back Matter
Reviews
“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)
Authors and Affiliations
About the author
Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T.J. Watson Research Center in Yorktown Heights, New York. He completed his B.S. from IIT Kanpur in 1993 and his Ph.D. from the Massachusetts Institute of Technology in 1996. He has published more than 300 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is author or editor of 15 books, including a textbook on data mining and a comprehensive book on outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He has received several internal and external awards, including the EDBT Test-of-Time Award (2014) and the IEEE ICDM Research Contributions Award (2015). He has also served as program or general chair of many major conferences in data mining. He is a fellow of the SIAM, ACM, and the IEEE, for “contributions to knowledge discovery and data mining algorithms.”
Bibliographic Information
Book Title: Recommender Systems
Book Subtitle: The Textbook
Authors: Charu C. Aggarwal
DOI: https://doi.org/10.1007/978-3-319-29659-3
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2016
Hardcover ISBN: 978-3-319-29657-9Published: 04 April 2016
Softcover ISBN: 978-3-319-80619-8Published: 25 April 2018
eBook ISBN: 978-3-319-29659-3Published: 28 March 2016
Edition Number: 1
Number of Pages: XXI, 498
Number of Illustrations: 61 b/w illustrations, 18 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Artificial Intelligence
Industry Sectors: Aerospace, Biotechnology, Consumer Packaged Goods, Electronics, Energy, Utilities & Environment, Engineering, Finance, Business & Banking, IT & Software, Law, Materials & Steel, Oil, Gas & Geosciences, Pharma, Telecommunications