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Recommender Systems by Charu C. Aggarwal (2016, Hardcover)

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Item specifics

Condition
Very Good
A book that has been read and does not look new, but is in excellent condition. No obvious damage to the book cover, with the dust jacket (if applicable) included for hard covers. No missing or damaged pages, no creases or tears, no underlining or highlighting of text, and no writing in the margins. Some identifying marks on the inside cover, but this is minimal. Very little wear and tear. See the seller’s listing for full details and description of any imperfections. See all condition definitionsopens in a new window or tab
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“Some corner wear and marks on cover.”
ISBN
9783319296579
Subject Area
Computers, Mathematics
Publication Name
Recommender Systems
Item Length
10 in
Publisher
Springer International Publishing A&G
Subject
Expert Systems, Probability & Statistics / General, Intelligence (Ai) & Semantics, Databases / Data Mining
Publication Year
2016
Type
Textbook
Format
Hardcover
Language
English
Author
Charu C. Aggarwal
Item Width
7 in
Item Weight
392.3 Oz
Number of Pages
Xxi, 498 Pages

About this product

Product Information

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

Product Identifiers

Publisher
Springer International Publishing A&G
ISBN-10
3319296574
ISBN-13
9783319296579
eBay Product ID (ePID)
219220428

Product Key Features

Author
Charu C. Aggarwal
Publication Name
Recommender Systems
Format
Hardcover
Language
English
Subject
Expert Systems, Probability & Statistics / General, Intelligence (Ai) & Semantics, Databases / Data Mining
Publication Year
2016
Type
Textbook
Subject Area
Computers, Mathematics
Number of Pages
Xxi, 498 Pages

Dimensions

Item Length
10 in
Item Width
7 in
Item Weight
392.3 Oz

Additional Product Features

Number of Volumes
1 Vol.
Lc Classification Number
Qa76.9.D343
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)
Table of Content
An Introduction to Recommender Systems.- 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.
Copyright Date
2016
Illustrated
Yes

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