Recommender Systems: The Textbook

by Charu C. Aggarwal

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 Algorithms and   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 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   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.

Genres: Artificial Intelligence, Textbooks, Reference, Computer Science

519 pages, Hardcover

Published April 4, 2016

Reviews

[Walter Ullon · Rating 5 out of 5]

Excellent, comprehensive, accessible resource for an intermediate look at the taxonomy of recommender systems - a.k.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 more. 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.

It will not be my last book by Aggarwal. Highest recommendation.

[Terran M · Rating 5 out of 5]

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.

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 more challenging cases such as location or time-dependent recommendations.

Like all of Aggarwal's books, this one has an extensive bibliography so you can find more 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.

[Hamed Mansouri · Rating 4 out of 5]

کتاب خوبی بود ولی زیادی تو سبک (کتاب درسی) نوشته شده
موضوعات تعریف میشن و بدون نشون دادن ارتباط با سایر موضوعات مبحث تموم میشه
الان با وجود سایتای مختلف و قوی کسی کمبود تعریف موضوع نداره و کتابای علمی باید بیشتر روی ارتباط موضوعات و کاربرداشون بحث کنن
شما این کتاب رو در سطح آشنایی مقدماتی در نظر بگیرین

[Mihailo Joksimovic · Rating 5 out of 5]

This is really not a book that you can "read". I'd rather call it "Recommender Systems" encyclopedia and reference book.

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.

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.

I still haven't dived into the Knowledge-based systems and Advanced topics, but I'll leave that for some other time.