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Author: Santos, Olga C. Publisher: IGI Global ISBN: 161350490X Category : Education Languages : en Pages : 362
Book Description
Recommender systems have shown to be successful in many domains where information overload exists. This success has motivated research on how to deploy recommender systems in educational scenarios to facilitate access to a wide spectrum of information. Tackling open issues in their deployment is gaining importance as lifelong learning becomes a necessity of the current knowledge-based society. Although Educational Recommender Systems (ERS) share the same key objectives as recommenders for e-commerce applications, there are some particularities that should be considered before directly applying existing solutions from those applications. Educational Recommender Systems and Technologies: Practices and Challenges aims to provide a comprehensive review of state-of-the-art practices for ERS, as well as the challenges to achieve their actual deployment. Discussing such topics as the state-of-the-art of ERS, methodologies to develop ERS, and architectures to support the recommendation process, this book covers researchers interested in recommendation strategies for educational scenarios and in evaluating the impact of recommendations in learning, as well as academics and practitioners in the area of technology enhanced learning.
Author: Santos, Olga C. Publisher: IGI Global ISBN: 161350490X Category : Education Languages : en Pages : 362
Book Description
Recommender systems have shown to be successful in many domains where information overload exists. This success has motivated research on how to deploy recommender systems in educational scenarios to facilitate access to a wide spectrum of information. Tackling open issues in their deployment is gaining importance as lifelong learning becomes a necessity of the current knowledge-based society. Although Educational Recommender Systems (ERS) share the same key objectives as recommenders for e-commerce applications, there are some particularities that should be considered before directly applying existing solutions from those applications. Educational Recommender Systems and Technologies: Practices and Challenges aims to provide a comprehensive review of state-of-the-art practices for ERS, as well as the challenges to achieve their actual deployment. Discussing such topics as the state-of-the-art of ERS, methodologies to develop ERS, and architectures to support the recommendation process, this book covers researchers interested in recommendation strategies for educational scenarios and in evaluating the impact of recommendations in learning, as well as academics and practitioners in the area of technology enhanced learning.
Author: Nikos Manouselis Publisher: Springer Science & Business Media ISBN: 1493905309 Category : Computers Languages : en Pages : 306
Book Description
As 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.
Author: Nikos Manouselis Publisher: Springer Science & Business Media ISBN: 146144361X Category : Computers Languages : en Pages : 76
Book Description
Technology 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.
Author: Publisher: ISBN: Category : Educational technology Languages : en Pages : 328
Book Description
"This book aims to provide a comprehensive review of state-of-the-art practices for educational recommender systems, as well as the challenges to achieve their actual deployment"--Provided by publisher.
Author: Erik Duval Publisher: Springer ISBN: 3319026003 Category : Education Languages : en Pages : 180
Book Description
This book gives an overview of the state-of-the-art in Technology Enhanced Learning (TEL). It is organized as a collection of 14 research themes, each introduced by leading experts and including references to the most relevant literature on the theme of each cluster. Additionally, each chapter discusses four seminal papers on the theme with expert commentaries and updates. This volume is of high value to people entering the field of learning with technology, to doctoral students and researchers exploring the breadth of TEL, and to experienced researchers wanting to keep up with latest developments.
Author: Sachi Nandan Mohanty Publisher: John Wiley & Sons ISBN: 1119711592 Category : Computers Languages : en Pages : 448
Book Description
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 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.
Author: P. Pavan Kumar Publisher: CRC Press ISBN: 1000387372 Category : Computers Languages : en Pages : 182
Book Description
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 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.
Author: Gulzar, Zameer Publisher: IGI Global ISBN: 1799850102 Category : Education Languages : en Pages : 336
Book Description
Technology is currently playing a vital role in revolutionizing education systems and progressing academia into the digital age. Technological methods including data mining and machine learning are assisting with the discovery of new techniques for improving learning environments in regions across the world. As the educational landscape continues to rapidly transform, researchers and administrators need to stay up to date on the latest advancements in order to elevate the quality of teaching in their specific institutions. Machine Learning Approaches for Improvising Modern Learning Systems provides emerging research exploring the theoretical and practical aspects of technological enhancements in educational environments and the popularization of contemporary learning methods in developing countries. Featuring coverage on a broad range of topics such as game-based learning, intelligent tutoring systems, and course modelling, this book is ideally designed for researchers, scholars, administrators, policymakers, students, practitioners, and educators seeking current research on the digital transformation of educational institutions.
Author: Miltiadis D. Lytras Publisher: Springer ISBN: 3540877819 Category : Computers Languages : en Pages : 603
Book Description
It is a great pleasure to share with you the Springer LNCS proceedings of the First World Summit on the Knowledge Society - WSKS 2008 that was organized by the Open Research Society, NGO, http://www.open-knowledge-society.org, and took place in the American College of Greece, http://www.acg.gr, during September 24–27, 2008, in Athens, Greece. The World Summit on the Knowledge Society Series is an international attempt to promote a dialogue on the main aspects of a knowledge society toward a better world for all based on knowledge and learning. The WSKS Series brings together academics, people from industry, policy makers, politicians, government officers and active citizens to look at the impact of infor- tion technology, and the knowledge-based era it is creating, on key facets of today’s world: the state, business, society and culture. Six general pillars provide the constitutional elements of the WSKS series: • Social and Humanistic Computing for the Knowledge Society––Emerging Te- nologies and Systems for the Society and Humanity • Knowledge, Learning, Education, Learning Technologies and E-learning for the Knowledge Society • Information Technologies––Knowledge Management Systems––E-business and Enterprise Information Systems for the Knowledge Society • Culture and Cultural Heritage––Technology for Culture Management––Management of Tourism and Entertainment––Tourism Networks in the Knowledge Society • Government and Democracy for the Knowledge Society • Research and Sustainable Development in the Knowledge Society The summit provides a distinct, unique forum for cross-disciplinary fertilization of research, favoring the dissemination of research that is relevant to international re-
Author: Frank Kane Publisher: ISBN: Category : Languages : en Pages :
Book Description
Automated recommendations are everywhere: Netflix, Amazon, YouTube, and more. Recommender systems learn about your unique interests and show the products or content they think you'll like best. Discover how to build your own recommender systems from one of the pioneers in the field. Frank Kane spent over nine years at Amazon, where he led the development of many of the company's personalized product recommendation technologies. In this course, he covers recommendation algorithms based on neighborhood-based collaborative filtering and more modern techniques, including matrix factorization and even deep learning with artificial neural networks. Along the way, you can learn from Frank's extensive industry experience and understand the real-world challenges of applying these algorithms at a large scale with real-world data. You can also go hands-on, developing your own framework to test algorithms and building your own neural networks using technologies like Amazon DSSTNE, AWS SageMaker, and TensorFlow.
Author: Santos, Olga C.
Author: Santos, Olga C.
Author: Nikos Manouselis
Author: Nikos Manouselis
Author: 
Author: Erik Duval
Author: Sachi Nandan Mohanty
Author: P. Pavan Kumar
Author: Gulzar, Zameer
Author: Miltiadis D. Lytras
Author: Frank Kane