Cambridge University Press, 2016. — 293 p. — ISBN: 978-1-107-03607-9. 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...
Springer, 2016. — 498 p. — ISBN: 978-3-319-29657-9. 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,...
River Publishers, 2022. — 132 p. Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining presents novel algorithms for academic search, recommendation and association rule mining that have been developed and optimized for different commercial as well as academic purpose systems. Along with the design and implementation of...
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....
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....
Packt Publishing, 2018. — 204 p. — ISBN: 978-1-78899-375-3. Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Objectives Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250...
Packt Publishing, 2018. — 204 p. — ISBN: 978-1-78899-375-3. Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Objectives Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250...
Packt Publishing, 2018. — 204 p. — ISBN: 978-1-78899-375-3. Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Objectives Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250...
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....
Morgan and Claypool, 2014. — 198 p. — ISBN: 978-1627052573 The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices and preferences of users is a challenging task: real-world...
Springer, 2020. — 144 p. — (Lecture Notes in Social Networks). — ISBN: 978-3-030-55217-6. This book includes the proceedings of the first workshop on Recommender Systems in Fashion 2019. It presents a state of the art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail and fashion. The volume covers contributions...
Shelter Island: Manning Publications, 2019. — 432 p. 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 readers how...
Manning Publications, 2019. — 400 р. — ISBN: 978-1617292705. Online recommender systems help users find movies, jobs, restaurants—even romance! There’s an art in combining statistics, demographics, and query terms to achieve results that will delight them. Learn to build a recommender system the right way: it can make or break your application! Practical Recommender Systems...
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...
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...
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. — 176 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...
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, 2015. — 135 p. — ISBN: 1783554495, 9781783554492 Learn the art of building robust and powerful recommendation engines using R About This Book Learn to exploit various data mining techniques Understand some of the most popular recommendation techniques This is a step-by-step guide full of real-world examples to help you build and optimize recommendation engines...
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...
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/Publishing House of Electronics Industry, 2024. — 256 p. — ISBN 978-981-97-2580-9. Recommender systems, as a highly popular Artificial Intelligence (AI) technology in recent years, have been widely applied across various industries. They have transformed the way we interact with technology, influencing our choices and shaping our experiences. This book provides a...
Cambridge University Press, 2011. — 335 p. 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...
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, 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 Singapore, 2025. — 157 p. — (Machine Learning: Foundations, Methodologies, and Applications). — eBook ISBN 978-981-96-3212-1. First major book on advances and prospects of cross-device federated recommendation. Elaborates neural networks, privacy computing and federated issues in this topic. Enables readers to get a broad summary of the latest research developments....
CRC Press, 2021. — 249 p. — ISBN 978-0367631857. Recommender systems use information filtering to predict user preferences. They are becoming a vital part of e-business and are used in a wide variety of industries, ranging from entertainment and social networking to information technology, tourism, education, agriculture, healthcare, manufacturing, and retail. Recommender...
Springer, 2015. — 135 p.
This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly...
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...
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...
Wiley, 2020. — 448 p. — ISBN: 9781119711575. This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. It comprehensively covers the topic of...
Springer, 2014. — 109 p. — ISBN: 1493902857. Online social networks collect information from users' social contacts and their daily interactions (co-tagging of photos, co-rating of products etc.) to provide them with recommendations of new products or friends. Lately, technological progressions in mobile devices (i.e. smart phones) enabled the incorporation of geo-location data...
Birkhäuser, 2013. — 297 p. — Applied and Numerical Harmonic Analysis. — ISBN: 3319013203, 9783319013206 Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and...
Springer, 2024. — 314 p. — ISBN 978-3-031-42558-5. Сеансовые рекомендательные системы с использованием глубокого обучения This book focuses on the widespread use of deep neural networks and their various techniques in session-based recommender systems (SBRS). It presents the success of using Deep Learning techniques in many SBRS applications from different perspectives. For...
Springer, 2024. — 314 p. This book focuses on the widespread use of deep neural networks and their various techniques in session-based recommender systems (SBRS). It presents the success of using Deep Learning techniques in many SBRS applications from different perspectives. For this purpose, the concepts and fundamentals of SBRS are fully elaborated, and different Deep...
2nd ed. — Springer, 2015. — 1003 p. — ISBN: 9781489976369
This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the...
Springer, 2010. — 871 p. First comprehensive handbook dedicated entirely to the field of recommender systems Contains detailed algorithms and provides a Java source for all algorithms Contributed to by leading experts in the field The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are...
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...
3rd edition. — Springer, 2022. — 1053 p. — ISBN 978-1-0716-2196-7. 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...
Springer, 2011. — 842 p. Recommender Systems are software tools and techniques providing suggestions for items to be of use to a user. The suggestions provided are aimed at supporting their users in various decision-making processes. Development of recommender systems is a multi-disciplinary effort which involves experts from various fields such as Artificial intelligence,...
CRC Press, 2023. — 279 p. — (Серия: Intelligent Systems). — ISBN 978-1-032-33321-2. Рекомендательные системы: мультидисциплинарный подход Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for...
Springer, 2015. — 126 p. — ISBN: 9783319227344. Concepts such as link prediction, graph patterns, recommendation systems based on user reputation, strategic partner selection, collaborative systems and network formation based on ‘social brokers’ are presented. Chapters cover a wide range of models and algorithms, including graph models and a personalized PageRank model....
Boston: The MIT Press, 2020. — 306 p. 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...
New York: Springer, 2017. — 101 p. This book presents the algorithms used to provide recommendations by exploiting matrix factorization and tensor decomposition techniques. It highlights well-known decomposition methods for recommender systems, such as Singular Value Decomposition (SVD), UV-decomposition, Non-negative Matrix Factorization (NMF), etc. and describes in detail the...
Springer, 2017. — 101 p. — (Springer Briefs in Computer Science). — ISBN10: 3319413562. — ISBN13: 978-3319413563. This book presents the algorithms used to provide recommendations by exploiting matrix factorization and tensor decomposition techniques. It highlights well-known decomposition methods for recommender systems, such as Singular Value Decomposition (SVD),...
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...
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...
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, 2015. — 294 p. This edited volume offers a clear in-depth overview of research covering a variety of issues in social search and recommendation systems. Within the broader context of social network analysis it focuses on important and up-coming topics such as real-time event data collection, frequent-sharing pattern mining, improvement of computer-mediated...
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, 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, 2016. — 122 p. — (SpringerBriefs in Computer Science). — ISBN10: 9811007470. — ISBN13: 978-9811007477 This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the...
Springer, 2018. — 110 p. — (SpringerBriefs 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, it...
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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