Scrutable Adaptivity in Community-Enabled Web Portals

Fedor Bakalov

ISBN 10: 3659357073 / ISBN 13: 9783659357077

Published by LAP LAMBERT Academic Publishing Feb 2013, 2013

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 244 pp. Englisch.

Seller Inventory # 9783659357077

Bibliographic Details

• Title: Scrutable Adaptivity in Community-Enabled Web Portals
• Publisher: LAP LAMBERT Academic Publishing Feb 2013
• Publication Date: 2013
• Language: English
• ISBN 10: 3659357073
• ISBN 13: 9783659357077
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 15D millimeters 220 millimeters width by 150 millimeters height by 15 millimeters depth
• Weight: 381 grams

About this title

Synopsis

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.

About the Author

Fedor Bakalov received PhD in Computer Science from Friedrich Schiller University of Jena. During his doctoral research, he investigated the transparency and usability issues of adaptive systems. Fedor is author of over 25 peer-reviewed publications and was twice granted IBM PhD Fellowship award. He also holds Master and Bachelor of Engineering.