Python graphlab recommender systems

Recommender Systems: The Textbook, Springer, April 2016

Charu C. Aggarwal.

Comprehensive textbook on recommender systems: Table of Contents

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Buy hard-cover or PDF (for general public- PDF has embedded links for navigation on e-readers)

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This book covers the topic of recommender systems comprehensively, starting with the fundamentals and then exploring the advanced topics. The chapters of this book can be 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 multi-armed bandits, learning to rank, group systems, multi-criteria systems, and active learning systems, are discussed together with applications.

Although this book is primarily written as a textbook, it is recognized that a large portion of the audience will comprise industrial practitioners and researchers. Therefore, the book is also designed to be useful from an applied and reference point of view. Numerous examples and exercises have been provided.

Cost-effective methods for obtaining electronic and hardcopy versions

The book is available in both hardcopy (hardcover) and electronic versions. The hardcover is available at all the usual channels (e.g, Amazon, Barnes and Noble etc.), in Kindle format, and also directly from Springer in hardcopy and pdf format. The good thing about Springer is that electronic versions are often widely accessible at no cost to subscribing institutions, which is particularly convenient for students. My understanding is that a very large fraction of universities in North America, Europe, Australia, and New Zealand are subscribers, and a rapidly increasing number of universities in Asia are also subscribing. The electronic version is available at the following Springerlink pointer . For subscribing institutions click from a computer directly connected to your institution network to download the book for free. Springer uses the domain name of your computer to regulate access. To be eligible, your institution must subscribe to "e-book package english (Computer Science)" or "e-book package english (full collection)". If your institution is eligible, you will see a (free) `Download Book' button. Otherwise you will see a (paid) `Get Access' button. Sometimes you may be able to download it from your library e-collection, even when it is not Web-accessible from your institution. For those who prefer desk copies rather than electronic books, there are some very cost-effective methods to obtain a paperback MyCopy edition for $25 or less (subscribing institutions only). If you have ever published an article (even journal) with Springer, you are also entitled to an additional 40% author discount for any Springer book (including the $25 paperback edition) using the approach described here .

In general, for electronic versions, I highly recommend buying the PDF directly from springer over Amazon's Kindle edition. The PDF has embedded links that allows navigation over an e-reader, and will take about 9 MB on your device. Aside from this, one PDF allows you use over any device or computer. Since the PDFs are fully produced by Springer (rather than Amazon kindle, where Amazon plays a role in conversion), the look and feel is fully controlled by author and publisher. This makes the PDF versions of better quality than an Amazon Kindle.

About the Author

Charu 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 Massachusetts Institute of Technology in 1996. He has worked extensively in the field of data mining, with particular interests in data streams, privacy, uncertain data and social network analysis. He has published 15 (4 authored and 11 edited) books, over 300 papers in refereed venues, and has applied for or been granted over 80 patents. His h-index is 72. Because of the commercial value of the above-mentioned patents, he has received several invention achievement awards and has thrice been designated a Master Inventor at IBM. He is a recipient of an IBM Corporate Award (2003) for his work on bio-terrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, and two IBM Outstanding Technical Achievement Awards for his work on streaming systems and high-dimensional data analysis. He has received two best paper awards and an EDBT Test-of-Time Award (2014). He has received the IEEE ICDM Research Contributions Award (2015), which is one of two highest awards for research in the field of data mining. He has served as the general or program co-chair of the IEEE Big Data Conference (2014), the ICDM Conference (2015), the ACM CIKM Conference (2015), and the KDD Conference (2016). He also co-chaired the data mining track at the WWW Conference 2009. He served as an associate editor of the IEEE Transactions on Knowledge and Data Engineering from 2004 to 2008. He is an associate editor of the ACM Transactions on Knowledge Discovery and Data Mining Journal , an action editor of the Data Mining and Knowledge Discovery Journal , an associate editor of the IEEE Transactions on Big Data, and an associate editor of the Knowledge and Information Systems Journal. He is editor-in-chief of the ACM SIGKDD Explorations. He is a fellow of the SIAM (2015), ACM (2013) and the IEEE (2010) for "contributions to knowledge discovery and data mining techniques."

Solution Manual for Book

The solution manual for the book is available here from Springer. There is a link for the solution manual on this page. If you are an instructor, then you can obtain a copy. Please do not ask me directly for a copy of the solution manual. It can only be distributed by Springer.

Resources for book

The resources for this book will grow over time. Currently, I have not found time to prepare slides for teaching and will add them over time. I will also make the powerpoint figures of the book available soon. Meanwhile, I have added links to various sites on the internet where software is available for related material. In case you use the book and prepare slides, please try to share them on the internet. I can add a link from my site like the links below (with your name acknowledged of course).

Chapter 1: An Introduction to Recommender Systems