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International Journal of Cooperative Information Systems: Vol. 14, No. 04
Print ISSN: 0218-8430
Online ISSN: 1793-6365

 
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IMPROVING USER EXPERIENCE THROUGH RULE-BASED SERVICE CUSTOMIZATION

RICHARD HULL

Bell Labs, Lucent Technologies, 600 Mountain Avenue, Murray Hill, NJ 07974, USA

BHARAT KUMAR

Bell Labs, Lucent Technologies, 600 Mountain Avenue, Murray Hill, NJ 07974, USA

DANIEL LIEUWEN

Bell Labs, Lucent Technologies, 600 Mountain Avenue, Murray Hill, NJ 07974, USA

PETER F. PATEL-SCHNEIDER

Bell Labs, Lucent Technologies, 600 Mountain Avenue, Murray Hill, NJ 07974, USA

ARNAUD SAHUGUET

Bell Labs, Lucent Technologies, 600 Mountain Avenue, Murray Hill, NJ 07974, USA

SRIRAM VARADARAJAN

Bell Labs, Lucent Technologies, 600 Mountain Avenue, Murray Hill, NJ 07974, USA

AVINASH VYAS

Bell Labs, Lucent Technologies, 600 Mountain Avenue, Murray Hill, NJ 07974, USA

The web and converged services paradigm promises tremendous flexibility in the creation of rich composite services for enterprises and end users. The flexibility and richness offers the possibility of highly customized, individualized services for the end user and hence revenue generating services for service providers (e.g. ASPs, telecom network operators, ISPs). But how can end users (and enterprises) specify their preferences when a myriad of possibilities and potential circumstances need to be addressed? In this paper, we advocate a solution based on policy management where user preferences are specified through forms but translated into rules in a high-level policy language. This paper identifies the requirements for this kind of interpretation, and describes the Houdini system (developed at Bell Labs) which offers a rich rule-based language and a framework that supports intuitive, forms-based provisioning interfaces.

Cited by (2):
, . (2013) Mobile client-side personalization. 2013 International Conference on Privacy and Security in Mobile Systems (PRISMS), 1-4. [CrossRef]
, , , , , , . (2006) A Framework for Learning to Personalize Converged Services Involving Social Networks. 2006 IEEE Mountain Workshop on Adaptive and Learning Systems, 91-97. [CrossRef]