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Recommender Systems for Technology Enhanced Learning, Nikos Manouselis; Hendrik Drachsler; Katrien Verbe


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Автор: Nikos Manouselis; Hendrik Drachsler; Katrien Verbe
Название:  Recommender Systems for Technology Enhanced Learning
ISBN: 9781493946563
Издательство: Springer
Классификация:



ISBN-10: 1493946560
Обложка/Формат: Paperback
Страницы: 306
Вес: 0.45 кг.
Дата издания: 03.09.2016
Язык: English
Размер: 234 x 156 x 17
Основная тема: Computer Science
Подзаголовок: Research Trends and Applications
Ссылка на Издательство: Link
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Поставляется из: Германии
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.


Matrix and Tensor Factorization Techniques for 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: 7685.00 р.
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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.

Recommender Systems

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

Mining, Modeling, and Recommending `Things` in Social Media

Автор: Martin Atzmueller; Alvin Chin; Christoph Scholz; C
Название: Mining, Modeling, and Recommending `Things` in Social Media
ISBN: 3319147226 ISBN-13(EAN): 9783319147222
Издательство: Springer
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Цена: 5590.00 р.
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Описание: This book constitutes the thoroughly refereed joint post-workshop proceedings of the 4th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2013, held in Prague, Czech Republic, in September 2013, and the 4th International Workshop on Modeling Social Media, MSM 2013, held in Paris, France, in May 2013.

Recommender Systems Handbook

Автор: Francesco Ricci; Lior Rokach; Bracha Shapira
Название: Recommender Systems Handbook
ISBN: 1489977805 ISBN-13(EAN): 9781489977809
Издательство: Springer
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Цена: 25853.00 р.
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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.

Trust Networks for 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: 13974.00 р.
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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.

Social Web Artifacts for Boosting Recommenders

Автор: Cai-Nicolas Ziegler
Название: Social Web Artifacts for Boosting Recommenders
ISBN: 331900526X ISBN-13(EAN): 9783319005263
Издательство: Springer
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Цена: 19591.00 р.
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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.

Recommender Systems for Technology Enhanced Learning

Автор: Manouselis Nikos
Название: Recommender Systems for Technology Enhanced Learning
ISBN: 1493905295 ISBN-13(EAN): 9781493905294
Издательство: Springer
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Цена: 15372.00 р.
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Описание: Recommender Systems for Technology Enhanced Learning

Recommender Systems for the Social Web

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: 16977.00 р.
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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.

Technology-Enhanced Systems and Tools for Collaborative Learning Scaffolding

Автор: Thanasis Daradoumis; Santi Caball?; Angel A. Juan;
Название: Technology-Enhanced Systems and Tools for Collaborative Learning Scaffolding
ISBN: 3642267920 ISBN-13(EAN): 9783642267925
Издательство: Springer
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Цена: 23508.00 р.
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Описание: Technology-enhanced systems and computer-aided tools that support scaffolding in collaborative learning are key goals in the education research sector. This book covers a number of approaches to fostering functional collaborative learning and working online.

Methodologies and Intelligent Systems for Technology Enhanced Learning

Автор: Tania Di Mascio; Rosella Gennari; Pierpaolo Vittor
Название: Methodologies and Intelligent Systems for Technology Enhanced Learning
ISBN: 3319196316 ISBN-13(EAN): 9783319196312
Издательство: Springer
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Цена: 19591.00 р.
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Описание: Emotions and Inclusion in Co-Design at School: Let's Measure Them.- On-line Assessment of Pride and Shame: Relationships with Cognitive Dimensions in University Students.- New Experiments on the Nature of the Skillful Coping of a Draftsman.-Using ICT to Motivate Students in a Heterogeneous Programming Group.- Quality of Learning under an All-inclusive Approach.- Gaming and Robotics to Transforming Learning.- Smart Heart CABG Edu: First Prototype and Preliminary Evaluation.- Influence of Gaming Activities on Cognitive Performances.- Boosting Learning: Non-intrusive Monitoring of Student's Effciency.- Towards Children-Oriented Visual Representations for Temporal Relations.

Methodologies and Intelligent Systems for Technology Enhanced Learning

Автор: Mauro Caporuscio; Fernando De la Prieta; Tania Di
Название: Methodologies and Intelligent Systems for Technology Enhanced Learning
ISBN: 3319401645 ISBN-13(EAN): 9783319401645
Издательство: Springer
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Цена: 18167.00 р.
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Описание: The 6th International Conference in Methodologies and intelligent Systems for Technology Enhanced Learning held in Seville (Spain) is host by the University of Seville from 1st to 3rd June, 2016.

Methodologies and Intelligent Systems for Technology Enhanced Learning

Автор: Pierpaolo Vittorini; Rosella Gennari; Tania Di Mas
Название: Methodologies and Intelligent Systems for Technology Enhanced Learning
ISBN: 3319608185 ISBN-13(EAN): 9783319608181
Издательство: Springer
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Цена: 20962.00 р.
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Описание: This book presents the outcomes of the 7th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning (MIS4TEL`17), hosted by the Polytechnic of Porto, Portugal from 21 to 23 June 2017.


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