Recommender Systems Handbook, Francesco Ricci; Lior Rokach; Bracha Shapira

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
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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: Fatih Gedikli
Название: Recommender Systems and the Social Web
ISBN: 3658019476 ISBN-13(EAN): 9783658019471
Издательство: Springer
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Цена: 46570.00 T
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Описание: ГЇВїВЅ There is an increasing demand for recommender systems due to the information overload users are facing on the Web. A tag recommender algorithm is proposed which recommends tags for users to annotate their favorite online resources.

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: 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: Jos? J. Pazos Arias; Ana Fern?ndez Vilas; Rebeca P
Название: Recommender Systems for the Social Web
ISBN: 3642446272 ISBN-13(EAN): 9783642446276
Издательство: Springer
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Цена: 113180.00 T
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Описание: This book introduces opportunities and challenges that arise in the recommenders` area with the advent of Web 2.0. It presents the mains aspects in the Web 2.0 hype which have to be incorporated in traditional recommender systems.

Автор: Cai-Nicolas Ziegler
Название: Social Web Artifacts for Boosting Recommenders
ISBN: 331900526X ISBN-13(EAN): 9783319005263
Издательство: Springer
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Цена: 130610.00 T
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Описание: This book presents approaches for exploiting the rapidly expanding fountain of Social Web knowledge by means of classification taxonomies and trust networks, which are used to improve the performance of product-focused recommender systems.

Автор: Panagiotis Symeonidis; Andreas Zioupos
Название: Matrix and Tensor Factorization Techniques for Recommender Systems
ISBN: 3319413562 ISBN-13(EAN): 9783319413563
Издательство: Springer
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Цена: 51230.00 T
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Описание: This book provides a detailed theoretical mathematical background of matrix/tensor factorization techniques and a step-by-step analysis of each method on the basis of an integrated toy example that runs throughout all its chapters and helps the reader to understand the key differences among methods.

Автор: Manouselis Nikos
Название: Recommender Systems for Technology Enhanced Learning
ISBN: 1493905295 ISBN-13(EAN): 9781493905294
Издательство: Springer
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Цена: 102480.00 T
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Описание: Recommender Systems for Technology Enhanced Learning

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: 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: 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: Gerald Kembellec; Ghislaine Chartron; Imad Saleh
Название: Recommender Systems
ISBN: 1848217684 ISBN-13(EAN): 9781848217683
Издательство: Wiley
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Цена: 146730.00 T
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Описание: Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies.