Recommender Systems for Location-based Social Networks, Panagiotis Symeonidis; Dimitrios Ntempos; Yannis M

Автор: Charu C. Aggarwal
Название: Recommender Systems
ISBN: 3319296574 ISBN-13(EAN): 9783319296579
Издательство: Springer
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Цена: 62410.00 T
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Описание: An Introduction to Recommender Systems.- Neighborhood-Based Collaborative Filtering.- Model-Based Collaborative Filtering.- Content-Based Recommender Systems.- Knowledge-Based Recommender Systems.- Ensemble-Based and Hybrid Recommender Systems.- Evaluating Recommender Systems.- Context-Sensitive Recommender Systems.- Time- and Location-Sensitive Recommender Systems.- Structural Recommendations in Networks.- Social and Trust-Centric Recommender Systems.- Attack-Resistant Recommender Systems.- Advanced Topics in Recommender Systems.

Автор: Patricia Victor; Chris Cornelis; Martine De Cock
Название: Trust Networks for Recommender Systems
ISBN: 9491216392 ISBN-13(EAN): 9789491216398
Издательство: Springer
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Цена: 93160.00 T
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Описание: Featuring innovative contributions to the field such as a new bilattice-based model for trust and distrust, this book on a hot research topic is the first in-depth study of the potential of distrust in the emerging domain of trust-enhanced recommendation.

Автор: Sachi Nandan Mohanty; Jyotir Moy Chatterjee
Название: Recommender system with machine learning and artificial intelligence :
ISBN: 1119711576 ISBN-13(EAN): 9781119711575
Издательство: Wiley
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Цена: 193190.00 T
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Описание: 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.

Автор: Daniel Schall
Название: Social Network-Based Recommender Systems
ISBN: 3319227343 ISBN-13(EAN): 9783319227344
Издательство: Springer
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Цена: 93160.00 T
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Описание: Social Network-Based Recommender Systems is designed as a reference for professionals and researchers working in social network analysis and companies working on recommender systems.

Автор: Daniel Schall
Название: Social Network-Based Recommender Systems
ISBN: 3319372297 ISBN-13(EAN): 9783319372297
Издательство: Springer
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Цена: 74530.00 T
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Описание: Social Network-Based Recommender Systems is designed as a reference for professionals and researchers working in social network analysis and companies working on recommender systems.

Автор: Ricci, Francesco, Rokach, Lior, Shapira, Bracha
Название: Recommender Systems Handbook
ISBN: 1489976361 ISBN-13(EAN): 9781489976369
Издательство: Springer
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Цена: 232910.00 T
Наличие на складе: Невозможна поставка.
Описание: 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.

Автор: Dokoohaki Nima, Jaradat Shatha, Corona Pampнn Humberto Jesъs
Название: Recommender Systems in Fashion and Retail
ISBN: 3030661024 ISBN-13(EAN): 9783030661021
Издательство: Springer
Цена: 74530.00 T
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Описание: Chapter 1. The Importance of Brand Affinity in Luxury Fashion Recommendations.- Chapter 2. Probabilistic Color Modelling of Clothing Items.- Chapter 3. User Aesthetics Identification for Fashion Recommendations.- Chapter 4. Towards User-in-the-Loop Online Fashion Size Recommendation with Low Cognitive Load.- Chapter 5. Attention Gets You the Right Size and Fit in Fashion.- Chapter 6. The Ensemble-Building Challenge for Fashion Recommendation.- Chapter 7. Outfit Generation and Recommendation - An Experimental Study.- Chapter 8. Understanding Professional Fashion Stylists' Outfit Recommendation Process.

Автор: Olga C. Santos, Jesus G. Boticario
Название: Educational Recommender Systems and Technologies: Practices and Challenges
ISBN: 1613504896 ISBN-13(EAN): 9781613504895
Издательство: Mare Nostrum (Eurospan)
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Цена: 170010.00 T
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Описание: Provides a comprehensive review of state-of-the-art practices for ERS, as well as the challenges to achieve their actual deployment. It will help 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.

Автор: Negre
Название: Information and Recommender Systems
ISBN: 1848217544 ISBN-13(EAN): 9781848217546
Издательство: Wiley
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Цена: 146730.00 T
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Описание: 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.

Автор: Nikos Manouselis; Hendrik Drachsler; Katrien Verbe
Название: Recommender Systems for Technology Enhanced Learning
ISBN: 1493946560 ISBN-13(EAN): 9781493946563
Издательство: Springer
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Цена: 102480.00 T
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Описание: Collaborative Filtering Recommendation of Educational Content in Social Environments utilizing Sentiment Analysis Techniques.- Towards automated evaluation of learning resources inside repositories.- Linked Data and the Social Web as facilitators for TEL recommender systems in research and practice.- The Learning Registry: Applying Social Metadata for Learning Resource Recommendations.- A Framework for Personalised Learning-Plan Recommendations in Game-Based Learning.- An approach for an Affective Educational Recommendation Model.- The Case for Preference-Inconsistent Recommendations.- Further Thoughts on Context-Aware Paper Recommendations for Education.- Towards a Social Trust-aware Recommender for Teachers.- ALEF: from Application to Platform for Adaptive Collaborative Learning.- Two Recommending Strategies to enhance Online Presence in Personal Learning Environments.- Recommendations from Heterogeneous Sources in a Technology Enhanced Learning Ecosystem.- COCOON CORE: CO-Author Recommendations based on Betweenness Centrality and Interest Similarity.- Scientific Recommendations to Enhance Scholarly Awareness and Foster Collaboration.

Автор: Guang-quan Zhang, Jie Lu, Qian Zhang
Название: Recommender Systems: Advanced Developments
ISBN: 9811224625 ISBN-13(EAN): 9789811224621
Издательство: World Scientific Publishing
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Цена: 132000.00 T
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Описание: Recommender systems provide users (businesses or individuals) with personalized online recommendations of products or information, to address the problem of information overload and improve personalized services. Recent successful applications of recommender systems are providing solutions to transform online services for e-government, e-business, e-commerce, e-shopping, e-library, e-learning, e-tourism, and more.This unique compendium not only describes theoretical research but also reports on new application developments, prototypes, and real-world case studies of recommender systems. The comprehensive volume provides readers with a timely snapshot of how new recommendation methods and algorithms can overcome challenging issues. Furthermore, the monograph systematically presents three dimensions of recommender systems — basic recommender system concepts, advanced recommender system methods, and real-world recommender system applications.By providing state-of-the-art knowledge, this excellent reference text will immensely benefit researchers, managers, and professionals in business, government, and education to understand the concepts, methods, algorithms and application developments in recommender systems.

Автор: Aggarwal Charu C.
Название: Recommender Systems: The Textbook
ISBN: 331980619X ISBN-13(EAN): 9783319806198
Издательство: Springer
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Цена: 62410.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Recommender Systems.- Neighborhood-Based Collaborative Filtering.- Model-Based Collaborative Filtering.- Content-Based Recommender Systems.- Knowledge-Based Recommender Systems.- Ensemble-Based and Hybrid Recommender Systems.- Evaluating Recommender Systems.- Context-Sensitive Recommender Systems.- Time- and Location-Sensitive Recommender Systems.- Structural Recommendations in Networks.- Social and Trust-Centric Recommender Systems.- Attack-Resistant Recommender Systems.- Advanced Topics in Recommender Systems.