TEST - Catálogo BURRF
   

Recommender systems handbook / edited by Francesco Ricci, Lior Rokach, Bracha Shapira.

Colaborador(es): Tipo de material: TextoTextoEditor: Boston, MA : Springer US : Springer, 2015Edición: 2nd ed. 2015Descripción: xvii, 1003 páginas : 144 ilustraciones, 78 ilustraciones en colorTipo de contenido:
  • texto
Tipo de medio:
  • computadora
Tipo de portador:
  • recurso en línea
ISBN:
  • 9781489976376
Formatos físicos adicionales: Edición impresa:: Sin títuloClasificación LoC:
  • QA75.5-76.95
Recursos en línea:
Contenidos:
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.
Valoración
    Valoración media: 0.0 (0 votos)
No hay ítems correspondientes a este registro

Springer eBooks

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.

Para consulta fuera de la UANL se requiere clave de acceso remoto.

Universidad Autónoma de Nuevo León
Secretaría de Extensión y Cultura - Dirección de Bibliotecas @
Soportado en Koha