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Please use this identifier to cite or link to this item: http://hdl.handle.net/2262/40536

Title: Adaptive Retrieval and Composition of Socio-Semantic Content for Personalised Customer Care
Other Titles: CEUR Workshop Proceedings
International Workshop on Adaptation in Social and Semantic Web (SAS-WEB 2010) in connection with UMAP 2010
Author: WADE, VINCENT PATRICK
STEICHEN, BEN
Sponsor: Science Foundation Ireland
Author's Homepage: http://people.tcd.ie/steicheb
http://people.tcd.ie/vwade
http://people.tcd.ie/steicheb
Keywords: Adaptive Information Retrieval
Adaptive Result Composition
Personalized Search
Socio-Semantic Search
Issue Date: 2010
Publisher: CEUR-WS.org
Citation: Ben Steichen, Vincent Wade, Adaptive Retrieval and Composition of Socio-Semantic Content for Personalised Customer Care, CEUR Workshop Proceedings, International Workshop on Adaptation in Social and Semantic Web (SAS-WEB 2010) in connection with UMAP 2010, Waikoloa, HI, USA, June 21, 2010, Federica Cena, Antonina Dattolo, Styliani Kleanthous, Carlo Tasso, David Bueno Vallejo, and Julita Vassileva, CEUR-WS.org, 2010, 1 - 10
Abstract: The parallel rise of the Semantic and Social Web provides unprecedented possibilities for the development of novel search system architectures. However, many traditional search systems have so far followed a simple one-size-fits-all paradigm by ignoring the different user information needs, preferences and intentions. In the last number of years, we have begun to see initial evidence that personalisation may be applied within web search engines, however little detail has been published other than adaptation based on user histories. Moreover, current implementations often fail to combine the mutual benefits of both structured and unstructured information resources. This paper presents techniques and architectures for leveraging socio-semantic content and adaptively retrieving and composing such content in order to provide personalised result presentations. The system is presented in a customer care scenario, which provides an application area for personalisation in terms of available heterogeneous resources as well as user preferences, context and characteristics. The presented architectures combine techniques from the fields of Information Retrieval, Semantic Search as well as Adaptive Hypermedia in order to enable efficient adaptive retrieval as well as personalised compositions.
Description: PUBLISHED
Waikoloa, HI, USA
URI: http://hdl.handle.net/2262/40536
Related links: http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-590/
Appears in Collections:Computer Science (Scholarly Publications)

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