Recommender Systems Handbook

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The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be...

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Détails bibliographiques
Cote:Libro Electrónico
Collectivité auteur: SpringerLink (Online service)
Autres auteurs: Ricci, Francesco (Éditeur intellectuel), Rokach, Lior (Éditeur intellectuel), Shapira, Bracha (Éditeur intellectuel), Kantor, Paul B. (Éditeur intellectuel)
Format: Électronique eBook
Langue:Inglés
Publié: New York, NY : Springer US : Imprint: Springer, 2011.
Édition:1st ed. 2011.
Sujets:
Artificial intelligence.
Information storage and retrieval systems.
Data mining.
Electronic commerce.
User interfaces (Computer systems).
Human-computer interaction.
Database management.
Artificial Intelligence.
Information Storage and Retrieval.
Data Mining and Knowledge Discovery.
e-Commerce and e-Business.
User Interfaces and Human Computer Interaction.
Database Management.
Accès en ligne:Texto Completo
Table des matières:
  • Introduction to Recommender Systems Handbook
  • Part I Basic Techniques
  • Data Mining Methods for Recommender Systems
  • Content-based Recommender Systems: State of the Art and Trends
  • A Comprehensive Survey of Neighborhood-based Recommendation Methods
  • Advances in Collaborative Filtering
  • Developing Constraint-based Recommenders
  • Context-Aware Recommender Systems
  • Part II Applications and Evaluation of RSs
  • Evaluating Recommendation Systems
  • A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment
  • How to Get the Recommender Out of the Lab?
  • Matching Recommendation Technologies and Domains
  • Recommender Systems in Technology Enhanced Learning
  • Part III Interacting with Recommender Systems
  • On the Evolution of Critiquing Recommenders
  • Creating More Credible and Persuasive Recommender Systems: The Influence of Source Characteristics on Recommender System Evaluations
  • Designing and Evaluating Explanations for Recommender Systems
  • Usability Guidelines for Product Recommenders Based on Example Critiquing Research
  • Map Based Visualization of Product Catalogs
  • Part IV Recommender Systems and Communities
  • Communities, Collaboration, and Recommender Systems in Personalized Web Search
  • Social Tagging Recommender Systems
  • Trust and Recommendations
  • Group Recommender Systems: Combining Individual Models
  • Aggregation of Preferences in Recommender Systems
  • Active Learning in Recommender Systems
  • Multi-Criteria Recommender Systems
  • Robust Collaborative Recommendation
  • Index.

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