If you’re new to the field of recommender systems, starting with a textbook is often a good approach. Textbooks provide a structured and comprehensive overview of key concepts, algorithms, and applications. Below, we recommend four core textbooks that serve as excellent introductions to recommender systems.

Standard Text Books on Recommender Systems

1. Recommender Systems Handbook (3rd edt.; 2022)

Written/Edited by Francesco Ricci, Lior Rokach, Bracha Shapira who are some of the most renowned researchers in the community, with Francesco Ricci being e.g. on the steering committee of the ACM Conference on Recommender Systems.

The third edition of this handbook provides an in-depth exploration of both classical and contemporary methods in recommender systems. It is structured into five sections: foundational recommendation techniques, advanced methods, evaluation and impact, human-computer interaction, and applications.

The first section covers core techniques widely used in recommender systems today, including collaborative filtering, semantic-based methods, systems leveraging implicit feedback, neural networks, and context-aware approaches. These are the building blocks for most modern recommender systems.

The second section delves into more specialized and emerging approaches, such as session-based recommendations, adversarial machine learning, group and reciprocal recommender systems, natural language processing techniques, and cross-domain recommendation methods.

The third section offers a broad perspective on the evaluation of recommender systems, focusing on methods for assessing their effectiveness, value, and impact. It also discusses multi-stakeholder perspectives and key metrics like fairness, novelty, and diversity.

2. Recommender Systems: An Introduction (2010)

Written by some of the most distinguished professors in the recommender-system community, namely Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich, with the first three authors all being on the steering committee of the ACM Conference on Recommender Systems. Even though the book is a bit outdated (from 2010) it still covers all the basics and is a worthwhile read.

In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.

https://www.amazon.com/Recommender-Systems-Dietmar-Jannach-ebook/dp/B00AKE1XZC/

3. Recommender Systems: The Textbook (2016)

Written by Charu C. Aggarwal, who is not as well known in the (academic) community as the authors of the previous two books. However, he has a strong recommender-systems industry background, working at IBM, and being the author of numerous books relating to deep learning and data mining. For more details on the author see http://www.charuaggarwal.net/

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: 1. 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. 2. 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. 3. 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.

https://www.amazon.com/Recommender-Systems-Textbook-Charu-Aggarwal-ebook/dp/B01DK3GZDY/

4. Practical Recommender Systems (2019)

Kim Falk is an expert in recommender systems with extensive experience in both academia and industry. He is known for his work on applying machine learning and statistical methods to build personalized recommendation engines. Kim frequently gives keynotes and invited talks at conferences, sharing insights on practical implementations and advancements in the field. His expertise spans various domains, including e-commerce and content recommendation.

The book “Practical Recommender Systems” by Kim Falk is a comprehensive guide to understanding and building effective recommender systems. The book walks readers through the key principles behind recommendation engines, which are used to suggest everything from movies to restaurants and even job opportunities. Falk emphasizes the importance of combining statistics, demographics, and user behavior data to produce accurate, personalized recommendations that can enhance user experience and engagement. The book is structured to help developers implement these systems correctly, covering everything from basic concepts to the scaling challenges that arise as systems grow.

The book provides a hands-on approach, demonstrating real-world examples in Python and detailing popular algorithms like collaborative filtering and content-based methods. It also covers advanced topics such as matrix factorization and hybrid recommenders, offering practical solutions to common issues developers face. Geared toward those with intermediate programming and database skills, Practical Recommender Systems is a valuable resource for developers and data scientists looking to implement scalable and personalized recommendation engines on their platforms.

Further Text Books on Recommender Systems

Hands-On Recommendation Systems with Python: Start building powerful and personalized, recommendation engines with Python (2018)

Rounak Banik
Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it’s friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you’ll get started with building and learning about recommenders as quickly as possible. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You’ll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques. With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you’re finished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains.

https://www.amazon.com/Hands-Recommendation-Systems-Python-recommendation-ebook/dp/B07G497Y9Y

Building Recommender Systems with Machine Learning and AI: Help people discover new products and content with deep learning, neural networks, and machine learning recommendations (2018)

Frank Kane
Learn how to build recommender systems from one of Amazon’s pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon’s personalized product recommendation technologies. You’ve seen automated recommendations everywhere – on Netflix’s home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you’ll become very valuable to them. This book is adapted from Frank’s popular online course published by Sundog Education, so you can expect lots of visual aids from its slides and a conversational, accessible tone throughout the book. The graphics and scripts from over 300 slides are included, and you’ll have access to all of the source code associated with it as well. We’ll cover tried and true recommendation algorithms based on neighborhood-based collaborative filtering, and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks. Along the way, you’ll learn from Frank’s extensive industry experience to understand the real-world challenges you’ll encounter when applying these algorithms at large scale and with real-world data. This book is very hands-on; you’ll develop your own framework for evaluating and combining many different recommendation algorithms together, and you’ll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people.

The author, Frank Kane, spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology. In 2016, he created Sundog Education, which offers popular online courses in the fields of data science, machine learning, data streaming, and “big data”. Over 150,000 students worldwide have enrolled in Frank’s courses.

https://www.amazon.com/Building-Recommender-Systems-Machine-Learning-ebook/dp/B07GCV5JCZ/

Recommendation Engines (2020)

How companies like Amazon and Netflix know what “you might also like”: the history, technology, business, and social impact of online recommendation engines. Increasingly, our technologies are giving us better, faster, smarter, and more personal advice than our own families and best friends. Amazon already knows what kind of books and household goods you like and is more than eager to recommend more; YouTube and TikTok always have another video lined up to show you; Netflix has crunched the numbers of your viewing habits to suggest whole genres that you would enjoy. In this volume in the MIT Press’s Essential Knowledge series, innovation expert Michael Schrage explains the origins, technologies, business applications, and increasing societal impact of recommendation engines, the systems that allow companies worldwide to know what products, services, and experiences “you might also like.” Schrage offers a history of recommendation that reaches back to antiquity’s oracles and astrologers; recounts the academic origins and commercial evolution of recommendation engines; explains how these systems work, discussing key mathematical insights, including the impact of machine learning and deep learning algorithms; and highlights user experience design challenges. He offers brief but incisive case studies of the digital music service Spotify; ByteDance, the owner of TikTok; and the online personal stylist Stitch Fix. Finally, Schrage considers the future of technological recommenders: Will they leave us disappointed and dependent—or will they help us discover the world and ourselves in novel and serendipitous ways?

https://mitpress.mit.edu/books/recommendation-engines

Machine Learning with PySpark: With Natural Language Processing and Recommender Systems (2018)

Pramod Singh
Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark. 
Machine Learning with PySpark shows you how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You’ll also see unsupervised machine learning models such as K-means and hierarchical clustering. A major portion of the book focuses on feature engineering to create useful features with PySpark to train the machine learning models. The natural language processing section covers text processing, text mining, and embedding for classification.  After reading this book, you will understand how to use PySpark’s machine learning library to build and train various machine learning models. Additionally you’ll become comfortable with related PySpark components, such as data ingestion, data processing, and data analysis, that you can use to develop data-driven intelligent applications.

https://www.amazon.com/Machine-Learning-PySpark-Processing-Recommender-ebook/dp/B07LDMMMFP/

Statistical Methods for Recommender Systems (2016)

Deepak K Agarwal, Bee-Chung Chen
Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users’ responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.

https://www.amazon.com/Statistical-Methods-Recommender-Systems-Agarwal-ebook/dp/B018MFKRXO/

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