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Reclaiming Personalized Learning: A Pedagogy for Restoring Equity and Humanity in Our Classrooms
by Paul Emerich FranceWhere exactly did personalized learning go so wrong? For teacher and consultant Paul France, at first technology-powered personalized learning seemed like a panacea. But after three years spent at a personalized learning start-up and network of microschools, he soon realized that such corporate-driven individualized learning initiatives do more harm than good, especially among our most vulnerable students. The far-superior alternative? A human-centered pedagogy that prioritizes children over technology. First, let’s be clear: Reclaiming Personalized Learning is not yet-another ed tech book. Instead it’s a user’s guide to restoring equity and humanity to our classrooms and schools through personalization. One part polemical, eleven parts practical, the book describes how to: Shape whole-class instruction, leverage small-group interactions, and nurture a student’s inner-dialogue Cultivate awareness within and among students, and build autonomy and authority Design curriculum with a flexible frame and where exactly the standards fit Humanize assessment and instruction, including the place of responsive teaching Create a sense of belonging, humanize technology integration, and effect socially just teaching and learning—all central issues in equity The truth is this: there’s no one framework, there’s no one tool that makes learning personalized–what personalized learning companies with a vested interest in profits might tempt you to believe. It’s people who personalize learning, and people not technology must be at the center of education. The time is now for all of us teachers to reclaim personalized learning, and this all-important book is our very best resource for getting started. “This is a compelling and critically important book for our time. With rich stories of teaching and learning Paul France considers ways to create the most positive learning experiences possible.” - JO BOALER, Nomellini & Olivier Professor of Education, Stanford Graduate School of Education “This brilliant book is a major contribution to the re-imagination of learning and teaching for the twenty-first century and should be essential reading for new and experienced teachers alike." - TONY WAGNER, Senior Research Fellow, Learning Policy Institute “In these troubled times, this book is more than a breath of fresh air, it is a call to action. Paul gives us an accessible and sophisticated book that explains how and why we should celebrate the humanity of every single student.” - JIM KNIGHT, Senior Partner of the Instructional Coaching Group (ICG) and Author of The Impact Cycle
Reclaiming Personalized Learning: A Pedagogy for Restoring Equity and Humanity in Our Classrooms
by Paul Emerich FranceWhere exactly did personalized learning go so wrong? For teacher and consultant Paul France, at first technology-powered personalized learning seemed like a panacea. But after three years spent at a personalized learning start-up and network of microschools, he soon realized that such corporate-driven individualized learning initiatives do more harm than good, especially among our most vulnerable students. The far-superior alternative? A human-centered pedagogy that prioritizes children over technology. First, let’s be clear: Reclaiming Personalized Learning is not yet-another ed tech book. Instead it’s a user’s guide to restoring equity and humanity to our classrooms and schools through personalization. One part polemical, eleven parts practical, the book describes how to: Shape whole-class instruction, leverage small-group interactions, and nurture a student’s inner-dialogue Cultivate awareness within and among students, and build autonomy and authority Design curriculum with a flexible frame and where exactly the standards fit Humanize assessment and instruction, including the place of responsive teaching Create a sense of belonging, humanize technology integration, and effect socially just teaching and learning—all central issues in equity The truth is this: there’s no one framework, there’s no one tool that makes learning personalized–what personalized learning companies with a vested interest in profits might tempt you to believe. It’s people who personalize learning, and people not technology must be at the center of education. The time is now for all of us teachers to reclaim personalized learning, and this all-important book is our very best resource for getting started. “This is a compelling and critically important book for our time. With rich stories of teaching and learning Paul France considers ways to create the most positive learning experiences possible.” - JO BOALER, Nomellini & Olivier Professor of Education, Stanford Graduate School of Education “This brilliant book is a major contribution to the re-imagination of learning and teaching for the twenty-first century and should be essential reading for new and experienced teachers alike." - TONY WAGNER, Senior Research Fellow, Learning Policy Institute “In these troubled times, this book is more than a breath of fresh air, it is a call to action. Paul gives us an accessible and sophisticated book that explains how and why we should celebrate the humanity of every single student.” - JIM KNIGHT, Senior Partner of the Instructional Coaching Group (ICG) and Author of The Impact Cycle
Reclaiming Personalized Learning: A Pedagogy for Restoring Equity and Humanity in Our Classrooms
by Paul Emerich FrancePut the person back in personalization with a touch of humanity. It’s a paradox: technology to individualize curriculum has made classrooms less personal. Let’s instead trust educators to make learning personal by supporting student agency, self-awareness, and the intimate personal connections found in authentic learning experiences. In the second edition of this groundbreaking book—newly streamlined, and updated with insights from the pandemic—Paul France presents a vision of humanized personalization that rejects the corporate mindset and instead holds equity and inclusion at its center. France leverages over a decade of experience as a National Board Certified Teacher, education consultant, and education technology developer, sharing the following: Practical guidance on designing inclusive learning environments for diverse groups Sustainable applications for humanized personalization in curriculum design, assessment, and instruction Real-life stories from the author’s experience on both sides of the personalization debate A multitude of classroom tools, adaptable to a variety of instructional contexts Nobody understands the need for humanizing education better than teachers. While educators across the country have learned that inundating students with personalized learning technologies is not the way to go, many don’t know how to personalize learning without them. The time to humanize personalized learning and our classrooms is now—and this book will give you a place to start.
Reclaiming Personalized Learning: A Pedagogy for Restoring Equity and Humanity in Our Classrooms
by Paul Emerich FrancePut the person back in personalization with a touch of humanity. It’s a paradox: technology to individualize curriculum has made classrooms less personal. Let’s instead trust educators to make learning personal by supporting student agency, self-awareness, and the intimate personal connections found in authentic learning experiences. In the second edition of this groundbreaking book—newly streamlined, and updated with insights from the pandemic—Paul France presents a vision of humanized personalization that rejects the corporate mindset and instead holds equity and inclusion at its center. France leverages over a decade of experience as a National Board Certified Teacher, education consultant, and education technology developer, sharing the following: Practical guidance on designing inclusive learning environments for diverse groups Sustainable applications for humanized personalization in curriculum design, assessment, and instruction Real-life stories from the author’s experience on both sides of the personalization debate A multitude of classroom tools, adaptable to a variety of instructional contexts Nobody understands the need for humanizing education better than teachers. While educators across the country have learned that inundating students with personalized learning technologies is not the way to go, many don’t know how to personalize learning without them. The time to humanize personalized learning and our classrooms is now—and this book will give you a place to start.
Reclaiming Shilo Snow: The Pulse-Pounding Sequel to The Evaporation of Sofi Snow (Sofi Snow #2)
by Mary WeberShe was far more capable than Earth's leaders had accounted for, and they had no idea what she'd do next.“In this sequel to The Evaporation of Sofi Snow, Weber takes a darker tone, delving into alien abduction, experimentation on children, the machinations of power-hungry politicians, and black-market corruption . . . This is a well-paced page-turner.” —Kirkus ReviewsKnown as a brilliant mind that could hack her world’s darkest secrets, seventeen-year-old Sofi Snow is the most wanted teenager alive. She found her way to the icy, technologically brilliant planet of Delon to find Shilo, the brother everyone but Sofi believes is dead.But as she and Ambassador Miguel partner to find her brother and warn those on Earth of Delon’s dark designs on humanity, Sofi’s memories threaten to overtake her, distorting everything she holds true. She knows the Delonese once kept her in a dark, deceptive place . . . and destroyed a portion of her life. Now, the more they discover of Sofi’s past, the more Sofi feels herself unraveling—as each new revelation has her questioning the very existence of reality.In this harrowing sequel to The Evaporation of Sofi Snow, Sofi and Miguel must trust each other and discover the secrets locked inside Sofi’s mind as the line between what’s real and what they imagine begins to slip away . . . threatening to take the human race with it.
Recoding Gender: Women's Changing Participation in Computing (History of Computing)
by Janet AbbateThe untold history of women and computing: how pioneering women succeeded in a field shaped by gender biases.Today, women earn a relatively low percentage of computer science degrees and hold proportionately few technical computing jobs. Meanwhile, the stereotype of the male “computer geek” seems to be everywhere in popular culture. Few people know that women were a significant presence in the early decades of computing in both the United States and Britain. Indeed, programming in postwar years was considered woman's work (perhaps in contrast to the more manly task of building the computers themselves). In Recoding Gender, Janet Abbate explores the untold history of women in computer science and programming from the Second World War to the late twentieth century. Demonstrating how gender has shaped the culture of computing, she offers a valuable historical perspective on today's concerns over women's underrepresentation in the field.Abbate describes the experiences of women who worked with the earliest electronic digital computers: Colossus, the wartime codebreaking computer at Bletchley Park outside London, and the American ENIAC, developed to calculate ballistics. She examines postwar methods for recruiting programmers, and the 1960s redefinition of programming as the more masculine “software engineering.” She describes the social and business innovations of two early software entrepreneurs, Elsie Shutt and Stephanie Shirley; and she examines the career paths of women in academic computer science.Abbate's account of the bold and creative strategies of women who loved computing work, excelled at it, and forged successful careers will provide inspiration for those working to change gendered computing culture.
Recoding Gender
by Janet AbbateToday, women earn a relatively low percentage of computer science degrees and hold proportionately few technical computing jobs. Meanwhile, the stereotype of the male "computer geek" seems to be everywhere in popular culture. Few people know that women were a significant presence in the early decades of computing in both the United States and Britain. Indeed, programming in postwar years was considered woman's work (perhaps in contrast to the more manly task of building the computers themselves). In Recoding Gender, Janet Abbate explores the untold history of women in computer science and programming from the Second World War to the late twentieth century. Demonstrating how gender has shaped the culture of computing, she offers a valuable historical perspective on today's concerns over women's underrepresentation in the field. Abbate describes the experiences of women who worked with the earliest electronic digital computers: Colossus, the wartime codebreaking computer at Bletchley Park outside London, and the American ENIAC, developed to calculate ballistics. She examines postwar methods for recruiting programmers, and the 1960s redefinition of programming as the more masculine "software engineering." She describes the social and business innovations of two early software entrepreneurs, Elsie Shutt and Stephanie Shirley; and she examines the career paths of women in academic computer science. Abbate's account of the bold and creative strategies of women who loved computing work, excelled at it, and forged successful careers will provide inspiration for those working to change gendered computing culture.
Recommendation and Search in Social Networks
by Özgür Ulusoy Abdullah Uz Tansel Erol ArkunThis 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 communication, social tagging information, search system personalization, new detection mechanisms for the identification of online user groups, and many more. The twelve contributed chapters are extended versions of conference papers as well as completely new invited chapters in the field of social search and recommendation systems. This first-of-its kind survey of current methods will be of interest to researchers from both academia and industry working in the field of social networks.
Recommendation Engines (The MIT Press Essential Knowledge series)
by Michael SchrageHow companies like Amazon, Netflix, and Spotify know what "you might also like": the history, technology, business, and societal 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."
Recommendation Systems in Software Engineering
by Martin P. Robillard Walid Maalej Robert J. Walker Thomas ZimmermannWith the growth of public and private data stores and the emergence of off-the-shelf data-mining technology, recommendation systems have emerged that specifically address the unique challenges of navigating and interpreting software engineering data. This book collects, structures and formalizes knowledge on recommendation systems in software engineering. It adopts a pragmatic approach with an explicit focus on system design, implementation, and evaluation. The book is divided into three parts: "Part I - Techniques" introduces basics for building recommenders in software engineering, including techniques for collecting and processing software engineering data, but also for presenting recommendations to users as part of their workflow. "Part II - Evaluation" summarizes methods and experimental designs for evaluating recommendations in software engineering. "Part III - Applications" describes needs, issues and solution concepts involved in entire recommendation systems for specific software engineering tasks, focusing on the engineering insights required to make effective recommendations. The book is complemented by the webpage rsse. org/book, which includes free supplemental materials for readers of this book and anyone interested in recommendation systems in software engineering, including lecture slides, data sets, source code, and an overview of people, groups, papers and tools with regard to recommendation systems in software engineering. The book is particularly well-suited for graduate students and researchers building new recommendation systems for software engineering applications or in other high-tech fields. It may also serve as the basis for graduate courses on recommendation systems, applied data mining or software engineering. Software engineering practitioners developing recommendation systems or similar applications with predictive functionality will also benefit from the broad spectrum of topics covered.
Recommender System with Machine Learning and Artificial Intelligence: Practical Tools and Applications in Medical, Agricultural and Other Industries
by Sachi Nandan Mohanty Jyotir Moy Chatterjee Sarika Jain Ahmed A. Elngar Priya GuptaThis 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 recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare 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. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.
Recommender Systems
by Charu C. AggarwalThisbook comprehensively covers the topic of recommender systems, which providepersonalized recommendations of products or services to users based on theirprevious searches or purchases. Recommender system methods have been adapted todiverse applications including query log mining, social networking, newsrecommendations, and computational advertising. This book synthesizes bothfundamental and advanced topics of a research area that has now reachedmaturity. The chapters of this book are organized into three categories: - Algorithms and evaluation: Thesechapters discuss the fundamental algorithms in recommender systems, includingcollaborative filtering methods, content-based methods, knowledge-basedmethods, ensemble-based methods, and evaluation. - Recommendations in specific domains and contexts: the context of a recommendationcan be viewed as important side information that affects the recommendationgoals. Different types of context such as temporal data, spatial data, socialdata, tagging data, and trustworthiness are explored. - Advanced topics and applications: Various robustness aspects of recommender systems, such as shillingsystems, attack models, and their defenses are discussed. Inaddition, recent topics, such as learning to rank, multi-armed bandits, groupsystems, multi-criteria systems, and active learning systems, are introducedtogether with applications. Although this book primarily serves as atextbook, it will also appeal to industrial practitioners and researchers dueto its focus on applications and references. Numerous examples and exerciseshave been provided, and a solution manual is available for instructors.
Recommender Systems
by Dietmar Jannach Markus Zanker Alexander Felfernig Gerhard FriedrichIn 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.
Recommender Systems
by Gérald Kembellec Ghislaine Chartron Imad SalehAcclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales. On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior.
Recommender Systems: Frontiers and Practices
by Dongsheng Li Jianxun Lian Le Zhang Kan Ren Tun Lu Tao Wu Xing XieThis book starts from the classic recommendation algorithms, introduces readers to the basic principles and main concepts of the traditional algorithms, and analyzes their advantages and limitations. Then, it addresses the fundamentals of deep learning, focusing on the deep-learning-based technology used, and analyzes problems arising in the theory and practice of recommender systems, helping readers gain a deeper understanding of the cutting-edge technology used in these systems. Lastly, it shares practical experience with Microsoft 's open source project Microsoft Recommenders. Readers can learn the design principles of recommendation algorithms using the source code provided in this book, allowing them to quickly build accurate and efficient recommender systems from scratch.
Recommender Systems: Algorithms and Applications
by Sachi Nandan Mohanty P. Pavan Kumar S. Vairachilai Sirisha PotluriRecommender 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 Systems: Algorithms and Applications dives into the theoretical underpinnings of these systems and looks at how this theory is applied and implemented in actual systems. The book examines several classes of recommendation algorithms, including Machine learning algorithms Community detection algorithms Filtering algorithms Various efficient and robust product recommender systems using machine learning algorithms are helpful in filtering and exploring unseen data by users for better prediction and extrapolation of decisions. These are providing a wider range of solutions to such challenges as imbalanced data set problems, cold-start problems, and long tail problems. This book also looks at fundamental ontological positions that form the foundations of recommender systems and explain why certain recommendations are predicted over others. Techniques and approaches for developing recommender systems are also investigated. These can help with implementing algorithms as systems and include A latent-factor technique for model-based filtering systems Collaborative filtering approaches Content-based approaches Finally, this book examines actual systems for social networking, recommending consumer products, and predicting risk in software engineering projects.
Recommender Systems: A Multi-Disciplinary Approach (Intelligent Systems)
by Monideepa Roy Pushpendu Kar Sujoy DattaRecommender 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 different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data. Features of this book: Identifies and describes recommender systems for practical uses Describes how to design, train, and evaluate a recommendation algorithm Explains migration from a recommendation model to a live system with users Describes utilization of the data collected from a recommender system to understand the user preferences Addresses the security aspects and ways to deal with possible attacks to build a robust system This book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science.
Recommender Systems: Algorithms and their Applications (Transactions on Computer Systems and Networks)
by Monideepa Roy Pushpendu Kar Sujoy DattaThe 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 military surveillance. It demonstrates how to create attack-resistant and trust-centric recommender systems for sensitive data applications. This book provides a solid foundation for designing recommender systems for use in healthcare and defense.
Recommender Systems for Learning
by Katrien Verbert Hendrik Drachsler Erik Duval Nikos ManouselisTechnology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This brief attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains.
Recommender Systems for Location-based Social Networks
by Panagiotis Symeonidis Dimitrios Ntempos Yannis ManolopoulosOnline 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 in the traditional web-based online social networks, bringing the new era of Social and Mobile Web. The goal of this book is to bring together important research in a new family of recommender systems aimed at serving Location-based Social Networks (LBSNs). The chapters introduce a wide variety of recent approaches, from the most basic to the state-of-the-art, for providing recommendations in LBSNs. The book is organized into three parts. Part 1 provides introductory material on recommender systems, online social networks and LBSNs. Part 2 presents a wide variety of recommendation algorithms, ranging from basic to cutting edge, as well as a comparison of the characteristics of these recommender systems. Part 3 provides a step-by-step case study on the technical aspects of deploying and evaluating a real-world LBSN, which provides location, activity and friend recommendations. The material covered in the book is intended for graduate students, teachers, researchers, and practitioners in the areas of web data mining, information retrieval, and machine learning.
Recommender Systems for Medicine and Music (Studies in Computational Intelligence #946)
by Zbigniew W. Ras Alicja Wieczorkowska Shusaku TsumotoMusic recommendation systems are becoming more and more popular. The increasing amount of personal data left by users on social media contributes to more accurate inference of the user’s musical preferences and the same to quality of personalized systems. Health recommendation systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of valuable information at the right time by ensuring information quality, trustworthiness, authentication, and privacy concerns. Medical doctors deal with various kinds of diseases in which the music therapy helps to improve symptoms. Listening to music may improve heart rate, respiratory rate, and blood pressure in people with heart disease. Sound healing therapy uses aspects of music to improve physical and emotional health and well-being. The book presents a variety of approaches useful to create recommendation systems in healthcare, music, and in music therapy.
Recommender Systems for Technology Enhanced Learning
by Nikos Manouselis Hendrik Drachsler Katrien Verbert Olga C. SantosAs an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted increased interest during the past years. Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The goal is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices. Contributions address the following topics: i) user and item data that can be used to support learning recommendation systems and scenarios, ii) innovative methods and techniques for recommendation purposes in educational settings and iii) examples of educational platforms and tools where recommendations are incorporated.
Recommender Systems Handbook
by Francesco Ricci Lior Rokach Bracha ShapiraThis third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced within this field. It consists of five parts: general recommendation techniques, special recommendation techniques, value and impact of recommender systems, human computer interaction, and applications. The first part presents the most popular and fundamental techniques currently used for building recommender systems, such as collaborative filtering, semantic-based methods, recommender systems based on implicit feedback, neural networks and context-aware methods. The second part of this handbook introduces more advanced recommendation techniques, such as session-based recommender systems, adversarial machine learning for recommender systems, group recommendation techniques, reciprocal recommenders systems, natural language techniques for recommender systems and cross-domain approaches to recommender systems. The third part covers a wide perspective to the evaluation of recommender systems with papers on methods for evaluating recommender systems, their value and impact, the multi-stakeholder perspective of recommender systems, the analysis of the fairness, novelty and diversity in recommender systems. The fourth part contains a few chapters on the human computer dimension of recommender systems, with research on the role of explanation, the user personality and how to effectively support individual and group decision with recommender systems. The last part focusses on application in several important areas, such as, food, music, fashion and multimedia recommendation. This informative third edition handbook provides a comprehensive, yet concise and convenient reference source to recommender systems for researchers and advanced-level students focused on computer science and data science. Professionals working in data analytics that are using recommendation and personalization techniques will also find this handbook a useful tool.
Recommender Systems in Fashion and Retail (Lecture Notes in Electrical Engineering #734)
by Nima Dokoohaki Shatha Jaradat Humberto Jesús Corona Pampín Reza ShirvanyThis book includes the proceedings of the second workshop on recommender systems in fashion and retail (2020), and it aims to present a state-of-the-art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail, and fashion by presenting readers with chapters covering contributions from academic as well as industrial researchers active within this emerging new field. Recommender systems are often used to solve different complex problems in this scenario, such as product recommendations, or size and fit recommendations, and social media-influenced recommendations (outfits worn by influencers).
Recommender Systems in Fashion and Retail: Proceedings of the Third Workshop at the Recommender Systems Conference (2021) (Lecture Notes in Electrical Engineering #830)
by Nima Dokoohaki Shatha Jaradat Humberto Jesús Corona Pampín Reza ShirvanyThis book includes the proceedings of the third workshop on recommender systems in fashion and retail (2021), and it aims to present a state-of-the-art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail, and fashion by presenting readers with chapters covering contributions from academic as well as industrial researchers active within this emerging new field. Recommender systems are often used to solve different complex problems in this scenario, such as product recommendations, size and fit recommendations, and social media-influenced recommendations (outfits worn by influencers).