PDF Ebook Recommender Systems: The Textbook
PDF Ebook Recommender Systems: The Textbook
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Recommender Systems: The Textbook
PDF Ebook Recommender Systems: The Textbook
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Review
“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)
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From the Back Cover
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.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.â€
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Product details
Hardcover: 498 pages
Publisher: Springer; 1st ed. 2016 edition (March 29, 2016)
Language: English
ISBN-10: 3319296574
ISBN-13: 978-3319296579
Product Dimensions:
7 x 1.1 x 10 inches
Shipping Weight: 2.6 pounds (View shipping rates and policies)
Average Customer Review:
4.7 out of 5 stars
9 customer reviews
Amazon Best Sellers Rank:
#261,510 in Books (See Top 100 in Books)
For a technical book, the writing is surprisingly clear and the content is well-organized. This book has given me enough insight to build two sophisticated recommendation engines from scratch. I like that it does not get into specific languages and frameworks. It goes deep into the essential algorithms and mathematics that power recommendation engines and elucidates many concepts that are very hard to grasp by reading other sources. It gives you enough information to know what to look for when using a third party tool or framework. This is essential reading for anyone who wants to build a custom recommendation engine and actually understand how it works.I highly recommend it :)
I'm only at page 50 so far, but I LOVE this book! It's a great balance of theory and practical advice. I've been applying what I've learned by building some simple recommender systems using Python as I follow the textbook. I like some of the subtle details the author points out. For example, if you are building a simple neighborhood user-based collaborative filter system, you can find nearest neighbors by computing the Pearson similarity between user rows. For each row, you need to compute the mean rating. The author points out two ways this mean can be computed:(1) Compute the row mean of each user(2) Compute the row mean of each user but only at columns such that both users have values in those columnsThe Pearson correlation coefficient is defined to use option (2), but you can save computational time by using (1). Also, (1) might make more sense if (2) only yields very few values to estimate the mean for each user.It's subtle points like these I feel give me the confidence to grow my skillset in this area. I definitely recommend (no pun intended) to buy this book!
The authoritative book on recommender systems research, algorithms and system design. It lists a lot of the modern achievements in the space, and organizes and describes the math extremely well.
It's an excellent book which explains why of things. What could make it even better are some numerical examples and some sample R code. Also, Kindle version has TOC(table-of-contents) not hyperlinked makes it cumbersome to navigate. Regardless, it's both a solid text & reference
Great book and content.
Great book.
very complete book on recommender systems in nearly 500 pages of lucid writing.Almost every major topic is studied in detail. Matrix factorization material in the bookis lovely. The book can be helpful to both newcomers and advanced readers.
Excellent, comprehensive book. Pity there's no code/exercises/implementation examples.
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