Recommender Systems - Aggarwal, Charu C.

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Résumé :
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.

Biographie:
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 undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.D. in Operations Research from the Massachusetts Institute of Technology in 1996. He has published more than 400 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is author or editor of 20 books, including textbooks on linear algebra, machine learning (for text), neural networks, recommender systems, and 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), the ACM SIGKDD Innovation Award (2019), and the IEEE ICDM Research Contributions Award (2015). He is also a recipient of the W. Wallace McDowell Award, which is the highest technical honor given by IEEE Computer Society in the field of computer science. He has served as an editor-in-chief of the ACM SIGKDD Explorations. He is currently serving as the editor-in-chief of the ACM Transactions on Knowledge Discovery from Data and as an editor-in-chief of ACM Books. He is a fellow of the SIAM, ACM, and the IEEE, for contributions to knowledge discovery and data mining algorithms....

Sommaire:

Charu C. Aggarwalis 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.