Recommender systems / Charu C. Aggarwal

Por: Aggarwal, Charu C [,autor]Tipo de material: TextoTextoEditor: New York, NY : Springer, 2016Descripción: xxi, 498 páginasTipo de contenido: texto Tipo de medio: no mediado Tipo de portador: volumenISBN: 9783319296579Tema(s): CIENCIA DE LA COMPUTACION -- MINERIA DE DATOS | CIENCIA DE LA COMPUTACION -- INTELIGENCIA ARTIFICIALClasificación CDD: 004.5
Contenidos:
1.- An introduction to recommender systems -- 2.- Neighborhood-based collaborative filtering -- 3.- Model-based collaborative filtering -- 4.- Content-based recommender systems -- 5.- Knowledge-based recommender systems -- 6.- Ensemble-based and hybrid recommender systems -- 7.- Evaluating recommender systems -- 8.- Context-sensitive recommender systems -- 9.- Time and location-sensitive recommender systems -- 10.- Structural recommendations in networks -- 11.- Social and trust-centric recommender systems -- 12.- Attack-resistant recommender systems -- 13.- Advanced topics in recommender systems.-
Resumen: Thisbook comprehensively covers the topic of recommender systems, which providepersonalized recommendations of products or services to users based on theirprevious searches or purchases. Recommender system methods have been adapted todiverse applications including query log mining, social networking, newsrecommendations, and computational advertising. This book synthesizes bothfundamental and advanced topics of a research area that has now reachedmaturity. The chapters of this book are organized into three categories: - Algorithms and evaluation: Thesechapters discuss the fundamental algorithms in recommender systems, includingcollaborative filtering methods, content-based methods, knowledge-basedmethods, ensemble-based methods, and evaluation. - Recommendations in specific domains and contexts: the context of a recommendationcan be viewed as important side information that affects the recommendationgoals. Different types of context such as temporal data, spatial data, socialdata, tagging data, and trustworthiness are explored. - Advanced topics and applications: Various robustness aspects of recommender systems, such as shillingsystems, attack models, and their defenses are discussed. Inaddition, recent topics, such as learning to rank, multi-armed bandits, groupsystems, multi-criteria systems, and active learning systems, are introducedtogether with applications. Although this book primarily serves as atextbook, it will also appeal to industrial practitioners and researchers dueto its focus on applications and references. Numerous examples and exerciseshave been provided, and a solution manual is available for instructors.
Etiquetas de esta biblioteca: No hay etiquetas de esta biblioteca para este título. Ingresar para agregar etiquetas.
Valoración
    Valoración media: 0.0 (0 votos)
Existencias
Tipo de ítem Biblioteca actual Colección número de clasificación Copia número Estado Fecha de vencimiento Código de barras
Libro General Libro General Biblioteca Campus San Joaquín
Colección General 004.5 A266 (Navegar estantería(Abre debajo)) 1 Disponible 35609002083035

1.- An introduction to recommender systems -- 2.- Neighborhood-based collaborative filtering -- 3.- Model-based collaborative filtering -- 4.- Content-based recommender systems -- 5.- Knowledge-based recommender systems -- 6.- Ensemble-based and hybrid recommender systems -- 7.- Evaluating recommender systems -- 8.- Context-sensitive recommender systems -- 9.- Time and location-sensitive recommender systems -- 10.- Structural recommendations in networks -- 11.- Social and trust-centric recommender systems -- 12.- Attack-resistant recommender systems -- 13.- Advanced topics in recommender systems.-

Thisbook comprehensively covers the topic of recommender systems, which providepersonalized recommendations of products or services to users based on theirprevious searches or purchases. Recommender system methods have been adapted todiverse applications including query log mining, social networking, newsrecommendations, and computational advertising. This book synthesizes bothfundamental and advanced topics of a research area that has now reachedmaturity. The chapters of this book are organized into three categories:

- Algorithms and evaluation: Thesechapters discuss the fundamental algorithms in recommender systems, includingcollaborative filtering methods, content-based methods, knowledge-basedmethods, ensemble-based methods, and evaluation.

- Recommendations in specific domains and contexts: the context of a recommendationcan be viewed as important side information that affects the recommendationgoals. Different types of context such as temporal data, spatial data, socialdata, tagging data, and trustworthiness are explored.

- Advanced topics and applications: Various robustness aspects of recommender systems, such as shillingsystems, attack models, and their defenses are discussed.

Inaddition, recent topics, such as learning to rank, multi-armed bandits, groupsystems, multi-criteria systems, and active learning systems, are introducedtogether with applications.

Although this book primarily serves as atextbook, it will also appeal to industrial practitioners and researchers dueto its focus on applications and references. Numerous examples and exerciseshave been provided, and a solution manual is available for instructors.