Social Web Artifacts for Boosting Recommenders, Cai-Nicolas Ziegler
Автор: Fatih Gedikli Название: Recommender Systems and the Social Web ISBN: 3658019476 ISBN-13(EAN): 9783658019471 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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: 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: Martin Atzmueller; Alvin Chin; Christoph Scholz; C Название: Mining, Modeling, and Recommending `Things` in Social Media ISBN: 3319147226 ISBN-13(EAN): 9783319147222 Издательство: Springer Рейтинг: Цена: 5590.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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: 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: Masayuki Matsui Название: Fundamentals and Principles of Artifacts Science ISBN: 9811004722 ISBN-13(EAN): 9789811004728 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Описание: Corporations accumulate a lot of valuable data and knowledge over time, but storing and maintaining this data can be a logistic and financial headache for business leaders and IT specialists.Uncovering Essential Software Artifacts through Business Process Archaeology introduces an emerging method of software modernisation used to effectively manage legacy systems and company operations supported by such systems. This book presents methods, techniques, and new trends on business process archaeology as well as some industrial success stories. Business experts, professionals, and researchers working in the field of information and knowledge management will use this reference source to efficiently and effectively implement and utilise business knowledge.
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: 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: 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.
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