Recommender Systems: The Textbook By Charu C. Aggarwal Hardcover 3319296574 9783319296579 Recommender Systems: The Textbook 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.
PDF Recommender systems theory
498 This book is an extensive intermediate level survey of the literature in recommender systems organized by topic It is mathematically very accessible and provided you have read an introductory book about predictive models such as Introduction to Statistical Learning you should be able to follow it. Book recommender system github Aggarwal presents the tradeoffs between purely collaborative models using what other people think treating the item as an opaque ID content based models using meaningful properties of the item and guided search models and how to combine them There is a short but valuable section on learning to rank and then he extends to even challenging cases such as location or time dependent recommendations. Book recommender system xampp php Like all of Aggarwal s books this one has an extensive bibliography so you can find detials Unlike his Data Mining it presents few entirely new algorithms and instead talks about how to apply and reconfigure tools you already have for the specific case of recommendations and collaborative filtering Recommended 498 Excellent comprehensive accessible resource for an intermediate look at the taxonomy of recommender systems a. EPub Recommender systems theory a the science of similarity Everything is explained clearly pros and cons are weighted accordingly for each type of system and common pitfalls are given ample discussion The references alone are worth the price of the text I would not recommend it as an introduction to the subject as there s just not much in terms of worked out examples with sample datasets which always help motivate the material For that purpose I strongly recommend Practical Recommender Systems by Kim Falk I d keep Aggarwal as a reference to fill in the gaps though. Recommender systems in python It will not be my last book by Aggarwal Highest recommendation 498 This is really not a book that you can read I d rather call it Recommender Systems encyclopedia and reference book. Book Recommender systems theory I still haven t dived into the Knowledge based systems and Advanced topics but I ll leave that for some other time 498
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.
Book Recommender systems theory 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.
Recommender systems book pdf 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 Recommender Systems The Textbook.:
PDF Recommender systems theory The book is heavily math oriented which I actually liked Not because I m good in math but because it forced me to learn the math I needed to understand it:
Book Recommender systems All in all I think it took me around 3 4 months to actually digest it and to be able to actually code all the stuff that is present here:
Book Recommender systems Advanced topics and applications Various robustness aspects of recommender systems such as shilling systems attack models and their defenses are discussed.
Book recommender system xampp php 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.k