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
Рейтинг:
Цена: 16977.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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: Negre
Название: Information and Recommender Systems
ISBN: 1848217544 ISBN-13(EAN): 9781848217546
Издательство: Wiley
Рейтинг:
Цена: 22010.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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: Charu C. Aggarwal
Название: Recommender Systems
ISBN: 3319296574 ISBN-13(EAN): 9783319296579
Издательство: Springer
Рейтинг:
Цена: 9362.00 р.
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.

Автор: Guang-quan Zhang, Jie Lu, Qian Zhang
Название: Recommender Systems: Advanced Developments
ISBN: 9811224625 ISBN-13(EAN): 9789811224621
Издательство: World Scientific Publishing
Рейтинг:
Цена: 19800.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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: Francesco Ricci; Lior Rokach; Bracha Shapira
Название: Recommender Systems Handbook
ISBN: 1489977805 ISBN-13(EAN): 9781489977809
Издательство: Springer
Рейтинг:
Цена: 25853.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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.

Автор: Patricia Victor; Chris Cornelis; Martine De Cock
Название: Trust Networks for Recommender Systems
ISBN: 9491216392 ISBN-13(EAN): 9789491216398
Издательство: Springer
Рейтинг:
Цена: 13974.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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: Nikos Manouselis; Hendrik Drachsler; Katrien Verbe
Название: Recommender Systems for Technology Enhanced Learning
ISBN: 1493946560 ISBN-13(EAN): 9781493946563
Издательство: Springer
Рейтинг:
Цена: 15372.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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: Panagiotis Symeonidis; Andreas Zioupos
Название: Matrix and Tensor Factorization Techniques for Recommender Systems
ISBN: 3319413562 ISBN-13(EAN): 9783319413563
Издательство: Springer
Рейтинг:
Цена: 7685.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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: Prof. Dr. Klaus Peter Kaas; Tobias Schneider
Название: Preference-Based-Recommender-Systeme
ISBN: 3824483351 ISBN-13(EAN): 9783824483358
Издательство: Springer
Рейтинг:
Цена: 9794.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Автор: Tarnowska Katarzyna, Ras Zbigniew W., Daniel Lynn
Название: Recommender System for Improving Customer Loyalty
ISBN: 3030134407 ISBN-13(EAN): 9783030134402
Издательство: Springer
Рейтинг:
Цена: 13974.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book presents the Recommender System for Improving Customer Loyalty. The data mining techniques employed in the Recommender System allow users to "learn" from the experiences of others, without sharing proprietary information.

Автор: Manouselis Nikos
Название: Recommender Systems for Technology Enhanced Learning
ISBN: 1493905295 ISBN-13(EAN): 9781493905294
Издательство: Springer
Рейтинг:
Цена: 15372.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Recommender Systems for Technology Enhanced Learning

Автор: Aggarwal Charu C.
Название: Recommender Systems: The Textbook
ISBN: 331980619X ISBN-13(EAN): 9783319806198
Издательство: Springer
Рейтинг:
Цена: 9362.00 р.
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.