Scrutable Adaptivity in Community-Enabled Web Portals

Fedor Bakalov

ISBN 10: 3659357073 / ISBN 13: 9783659357077

Verlag: LAP LAMBERT Academic Publishing, 2013

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents a framework for scrutable adaptivity in community-enabled web portals. The framework provides four models that an adaptive system needs for personalization: (1) a user model representing information about individual users, (2) a metadata repository providing annotations of content, (3) domain model defining machine-processable semantics of the domain knowledge used for modeling user features and annotating content, and (4) personalization rules defining the logic of adaptation. Also, the framework provides methods for keeping these models up-to-date, complete, and accurate. Among others, these methods leverage the technology for Natural Language Processing and the willingness of the user community to contribute and annotate content. Furthermore, the framework provides graphical user interfaces and interaction patters for allowing users to view and adapt these models.

Bestandsnummer des Verkäufers 9783659357077

Bibliografische Details

• Titel: Scrutable Adaptivity in Community-Enabled Web Portals
• Verlag: LAP LAMBERT Academic Publishing
• Erscheinungsjahr: 2013
• Sprache: Englisch
• ISBN-10: 3659357073
• ISBN-13: 9783659357077
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 220B x 150 H x 15 T Millimeter Breite: 220 mm, Höhe: 150 mm, Tiefe: 15 mm
• Gewicht: 381 Gramm

Über diesen Titel

Inhaltsangabe

This book presents a framework for scrutable adaptivity in community-enabled web portals. The framework provides four models that an adaptive system needs for personalization: (1) a user model representing information about individual users, (2) a metadata repository providing annotations of content, (3) domain model defining machine-processable semantics of the domain knowledge used for modeling user features and annotating content, and (4) personalization rules defining the logic of adaptation. Also, the framework provides methods for keeping these models up-to-date, complete, and accurate. Among others, these methods leverage the technology for Natural Language Processing and the willingness of the user community to contribute and annotate content. Furthermore, the framework provides graphical user interfaces and interaction patters for allowing users to view and adapt these models.