Recommender Systems and the Social Web, Fatih Gedikli

Автор: Gerald Kembellec; Ghislaine Chartron; Imad Saleh
Название: Recommender Systems
ISBN: 1848217684 ISBN-13(EAN): 9781848217683
Издательство: Wiley
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Цена: 22010.00 р.
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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.

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.

Автор: Daniel Schall
Название: Social Network-Based Recommender Systems
ISBN: 3319372297 ISBN-13(EAN): 9783319372297
Издательство: Springer
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Цена: 11179.00 р.
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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: 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.

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.

Автор: Daniel Schall
Название: Social Network-Based Recommender Systems
ISBN: 3319227343 ISBN-13(EAN): 9783319227344
Издательство: Springer
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Цена: 13974.00 р.
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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: 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.