Matrix and Tensor Factorization Techniques for Recommender Systems, Panagiotis Symeonidis; Andreas Zioupos

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: 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: 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: 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: Steffen Rendle
Название: Context-Aware Ranking with Factorization Models
ISBN: 3642423973 ISBN-13(EAN): 9783642423970
Издательство: Springer
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Цена: 104480.00 T
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Описание: Context-aware ranking is an important task in search engine ranking. This book presents a generic method for context-aware ranking as well as its application. It applies this general theory to the three scenarios of item, tag and sequential-set recommendation.

Автор: Ganesh R. Naik
Название: Non-negative Matrix Factorization Techniques
ISBN: 3662483300 ISBN-13(EAN): 9783662483305
Издательство: Springer
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Цена: 74530.00 T
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Описание: This book collects new results, concepts and further developments of NMF. This book can be a good reference work for researchers and engineers interested in NMF, and can also be used as a handbook for students and professionals seeking to gain a better understanding of the latest applications of NMF.

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: Song Y. Yan
Название: Primality Testing and Integer Factorization in Public-Key Cryptography
ISBN: 1441945865 ISBN-13(EAN): 9781441945860
Издательство: Springer
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Цена: 153720.00 T
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Описание: Intended for advanced level students in computer science and mathematics, this key text, now in a brand new edition, provides a survey of recent progress in primality testing and integer factorization, with implications for factoring based public key cryptography.

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: Francesco Ricci; Lior Rokach; Bracha Shapira
Название: Recommender Systems Handbook
ISBN: 1489977805 ISBN-13(EAN): 9781489977809
Издательство: Springer
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Цена: 172350.00 T
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Описание: Recommender Systems: Introduction and Challenges.- A Comprehensive Survey of Neighborhood-based Recommendation Methods.- Advances in Collaborative Filtering.- Semantics-aware Content-based Recommender Systems.- Constraint-based Recommender Systems.- Context-Aware Recommender Systems.- Data Mining Methods for Recommender Systems.- Evaluating Recommender Systems.- Evaluating Recommender Systems with User Experiments.- Explaining Recommendations: Design and Evaluation.- Recommender Systems in Industry: A Netflix Case Study.- Panorama of Recommender Systems to Support Learning.- Music Recommender Systems.- The Anatomy of Mobile Location-Based Recommender Systems.- Social Recommender Systems.- People-to-People Reciprocal Recommenders.- Collaboration, Reputation and Recommender Systems in Social Web Search.- Human Decision Making and Recommender Systems.- Privacy Aspects of Recommender Systems.- Source Factors in Recommender System Credibility Evaluation.- Personality and Recommender Systems.- Group Recommender Systems: Aggregation, Satisfaction and Group Attributes.- Aggregation Functions for Recommender Systems.- Active Learning in Recommender Systems.- Multi-Criteria Recommender Systems.- Novelty and Diversity in Recommender Systems.- Cross-domain Recommender Systems.- Robust Collaborative Recommendation.