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The “Recommender Systems Handbook” is a comprehensive guide to the field of recommender systems, which is a subset of machine learning that is designed to predict the likelihood that a user will interact with an item. The book is written by leading experts in the field and provides an in-depth overview of the latest research and developments in the area. The third edition of the book was published in 2022 and covers a wide range of topics, including the basic concepts and techniques used in recommender systems, as well as the latest advances in the field.
The book begins with an introduction to the basic concepts of recommender systems, including the types of data and algorithms used, the evaluation of recommender systems, and the ethical and societal implications of these systems. The authors also provide a historical overview of the field, discussing the development of recommender systems from the early days of collaborative filtering to the latest advances in deep learning.
The book then delves into the different techniques used in recommender systems, including content-based filtering, collaborative filtering, and hybrid approaches. The authors provide a detailed explanation of these techniques, including the mathematical concepts and algorithms used, as well as their strengths and weaknesses. The book also covers more advanced topics such as deep learning for recommender systems, matrix factorization, and multi-armed bandits.
The third edition of the book also covers the latest advances in the field, including explainable AI, which is becoming increasingly important as recommender systems are used in more critical applications. There is also a section on the ethical and societal implications of recommender systems, which is becoming increasingly important as these systems are used in more sensitive areas such as healthcare and finance. The book also includes a chapter on industry case studies, which provides an insight into how recommender systems are used in real-world applications and the challenges faced by practitioners in the field.
Throughout the book, the authors use a combination of mathematical notation, pseudocode, and practical examples to help readers understand the concepts and techniques discussed. They also provide an extensive bibliography of relevant literature, making it a useful resource for researchers and practitioners in the field.
I am the founder of Recommender-Systems.com and head of the Intelligent Systems Group (ISG) at the University of Siegen, Germany https://isg.beel.org. We conduct research in recommender-systems (RecSys), personalization and information retrieval (IR) as well as on automated machine learning (AutoML), meta-learning and algorithm selection. Domains we are particularly interested in include smart places, eHealth, manufacturing (industry 4.0), mobility, visual computing, and digital libraries.
We founded or maintain, among others, LensKit-Auto, Darwin & Goliath, Mr. DLib, and Docear, each with thousand of users; we contributed to TensorFlow, JabRef and others; and we developed the first prototypes of automated recommender systems (AutoSurprise and Auto-CaseRec) and Federated Meta Learning (FMLearn Server and Client).