Packt Publishing, 2018. — 146 p. With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon....
Manning Publications, 2019. — 401 p. — ISBN: 978-1-617292-70-5. Recommender systems are practically a necessity for keeping a site's content current, useful, and interesting to visitors. Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Practical Recommender Systems goes behind the curtain to show...
Springer, 2025. — 166 p. This book systematically examines scalability and effectiveness challenges related to the application of graph convolutional networks (GCNs) in recommender systems. By effectively modeling graph structures, GCNs excel in capturing high-order relationships between users and items, enabling the creation of enriched and expressive representations. The book...
Springer, 2018. — 216 p. — (SpringerBriefs in Electrical and Computer Engineering). — ISBN10: 3319750666, ISBN13: 978-3319750668. This book presents group recommender systems, which focus on the determination of recommendations for groups of users. The authors summarize different technologies and applications of group recommender systems. They include an in-depth discussion of...
Packt Publishing, 2016. — 452 р. — ISBN: 978-1-78588-485-6. A recommendation engine (sometimes referred to as a recommender system) is a tool that lets algorithm developers predict what a user may or may not like among a list of given items. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are...
Springer, 2024. — 174 p. The book includes a thorough examination of the many types of algorithms for recommender systems, as well as a comparative analysis of them. It addresses the problem of dealing with the large amounts of data generated by the recommender system. The book also includes two case studies on recommender system applications in healthcare monitoring and...
Springer/House of Electronics Industry, 2024. — 292 p. — ISBN 978-981-99-8963-8. Рекомендательные системы: границы и практика Эта книга начинается с классических рекомендательных алгоритмов, знакомит читателей с основными принципами и основными концепциями традиционных алгоритмов, а также анализирует их преимущества и ограничения. This book starts from the classic...
3rd edition. — Springer, 2022. — 2894 p. — ISBN 978-1-0716-2197-4. This third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced. It consists of five parts: general recommendation techniques, special recommendation techniques, value and impact of recommender systems, human computer...
Scatterplot Press, 2018. — 121 p. Learn How to Make Your Own Recommender System in an Afternoon. Recommender systems are one of the most visible applications of machine learning and data mining today and their uncanny ability to convert our unspoken actions into presenting items we desire is both addicting and concerning. And whether recommender systems excite or scare you, the...
Springer, 2020. — 178 p. — ISBN: 978-981-15-2513-1 This book focuses on Web recommender systems, offering an overview of approaches to develop these state-of-the-art systems. It also presents algorithmic approaches in the field of Web recommendations by extracting knowledge from Web logs, Web page content and hyperlinks. Recommender systems have been used in diverse...
Springer, 2018. — 182 p. — (Springer Briefs in Computer Science). — ISBN10: 9811313482, 13 978-9811313486. This book systematically introduces Point-of-interest (POI) recommendations in Location-based Social Networks (LBSNs). Starting with a review of the advances in this area, the book then analyzes user mobility in LBSNs from geographical and temporal perspectives. Further,...
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