Information And Recommender Systems
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Recommender Systems
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Author : Charu C. Aggarwal
language : en
Publisher: Springer
Release Date : 2016-03-28
Recommender Systems written by Charu C. Aggarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-28 with Computers categories.
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, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific 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. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.
Collaborative Filtering Recommender Systems
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Author : Michael D. Ekstrand
language : en
Publisher: Now Publishers Inc
Release Date : 2011
Collaborative Filtering Recommender Systems written by Michael D. Ekstrand and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
Collaborative Filtering Recommender Systems discusses a wide variety of the recommender choices available and their implications, providing both practitioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.
E Commerce And Web Technologies
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Author : Christian Huemer
language : en
Publisher: Springer
Release Date : 2012-08-04
E Commerce And Web Technologies written by Christian Huemer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-04 with Business & Economics categories.
This book constitutes the refereed proceedings of the 13th International Conference on Electronic Commerce and Web Technologies (EC-Web) held in Vienna, Austria, in September 2012. The 15 full and four short papers accepted for EC-Web, selected from 45 submissions, were carefully reviewed based on their originality, quality, relevance, and presentation. They are organized into topical sections on recommender systems, security and trust, mining and semantic services, negotiation, and agents and business services.
Recommender Systems
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Author : P. Pavan Kumar
language : en
Publisher: CRC Press
Release Date : 2021-06-01
Recommender Systems written by P. Pavan Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-01 with Computers categories.
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.
Information Science And Applications Icisa 2016
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Author : Kuinam J. Kim
language : en
Publisher: Springer
Release Date : 2017-02-21
Information Science And Applications Icisa 2016 written by Kuinam J. Kim and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-21 with Technology & Engineering categories.
This book contains selected papers from the 7th International Conference on Information Science and Applications (ICISA 2016) and provides a snapshot of the latest issues encountered in technical convergence and convergences of security technology. It explores how information science is core to most current research, industrial and commercial activities and consists of contributions covering topics including Ubiquitous Computing, Networks and Information Systems, Multimedia and Visualization, Middleware and Operating Systems, Security and Privacy, Data Mining and Artificial Intelligence, Software Engineering, and Web Technology. The contributions describe the most recent developments in information technology and ideas, applications and problems related to technology convergence, illustrated through case studies, and reviews converging existing security techniques. Through this volume, readers will gain an understanding of the current state-of-the-art information strategies and technologies of convergence security. The intended readers are researchers in academia, industry and other research institutes focusing on information science and technology.
Recommender Systems Handbook
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Author : Francesco Ricci
language : en
Publisher: Springer
Release Date : 2015-11-17
Recommender Systems Handbook written by Francesco Ricci and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-17 with Computers categories.
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 existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.
Educational Recommender Systems And Technologies
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Author : Olga C. Santos
language : en
Publisher:
Release Date : 2012
Educational Recommender Systems And Technologies written by Olga C. Santos and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Educational technology categories.
"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.
Information And Recommender Systems
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Author : Elsa Nègre
language : en
Publisher: John Wiley & Sons
Release Date : 2015-10-02
Information And Recommender Systems written by Elsa Nègre and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-02 with Computers categories.
Information is an element of knowledge that can be stored, processed or transmitted. It is linked to concepts of communication, data, knowledge or representation. In a context of steady increase in the mass of information it is difficult to know what information to look for and where to find them. Computer techniques exist to facilitate this research and allow relevant information extraction. Recommendation systems introduced the notions inherent to the recommendation, based, inter alia, information search, filtering, machine learning, collaborative approaches. It also deals with the assessment of such systems and has various applications.
Fashion Recommender Systems
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Author : Nima Dokoohaki
language : en
Publisher: Springer
Release Date : 2021-11-06
Fashion Recommender Systems written by Nima Dokoohaki and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-06 with Science categories.
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 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 social fashion-based recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. The impact of social networks and the influence that fashion influencers have on the choices people make for shopping is undeniable. For instance, many people use Instagram to learn about fashion trends from top influencers, which helps them to buy similar or even exact outfits from the tagged brands in the post. When traced, customers’ social behavior can be a very useful guide for online shopping websites, providing insights on the styles the customers are really interested in, and hence aiding the online shops in offering better recommendations and facilitating customers quest for outfits. Another well known difficulty with recommendation of similar items is the large quantities of clothing items which can be considered similar, but belong to different brands. Relying only on implicit customer behavioral data will not be sufficient in the coming future to distinguish between for recommendation that will lead to an item being purchased and kept, vs. a recommendation that might result in either the customer not following it, or eventually return the item. Finding the right size and fit for clothes is one of the major factors not only impacting customers purchase decision, but also their satisfaction from e-commerce fashion platforms. Moreover, fashion articles have important sizing variations. Finally, customer preferences towards perceived article size and fit for their body remain highly personal and subjective which influences the definition of the right size for each customer. The combination of the above factors leaves the customers alone to face a highly challenging problem of determining the right size and fit during their purchase journey, which in turn has resulted in having more than one third of apparel returns to be caused by not ordering the right article size. This challenge presents a huge opportunity for research in intelligent size and fit recommendation systems and machine learning solutions with direct impact on both customer satisfaction and business profitability.
Knowledge Recommendation Systems With Machine Intelligence Algorithms
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Author : Jarosław Protasiewicz
language : en
Publisher: Springer Nature
Release Date : 2023-09-30
Knowledge Recommendation Systems With Machine Intelligence Algorithms written by Jarosław Protasiewicz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-30 with Computers categories.
Knowledge recommendation is an timely subject that is encountered frequently in research and information services. A compelling and urgent need exists for such systems: the modern economy is in dire need of highly-skilled professionals, researchers, and innovators, who create opportunities to gain competitive advantage and assist in the management of financial resources and available goods, as well as conducting fundamental and applied research more effectively. This book takes readers on a journey into the world of knowledge recommendation, and of systems of knowledge recommendation that use machine intelligence algorithms. It illustrates knowledge recommendation using two examples. The first is the recommendation of reviewers and experts who can evaluate manuscripts of academic articles, or of research and development project proposals. The second is innovation support, which involves bringing science and business together by recommending information that pertains to innovations, projects, prospective partners, experts, and conferences meaningfully. The book also describes the selection of the algorithms that transform data into information and then into knowledge, which is then used in the information systems. More specifically, recommendation and information extraction algorithms are used to acquire data, classify publications, identify (disambiguate) their authors, extract keywords, evaluate whether enterprises are innovative, and recommend knowledge. This book comprises original work and is unique in many ways. The systems and algorithms it presents are informed by contemporary solutions described in the literature - including many compelling, novel, and original aspects. The new and promising directions the book presents, as well as the techniques of machine learning applied to knowledge recommendation, are all original.