ISSN : 1796-203X
Volume : 2    Issue : 6    Date : August 2007

Intuitive Network Applications: Learning for Personalized Converged Services Involving Social Networks
Robert Dinoff, Tin Kam Ho, Richard Hull, Bharat Kumar, Daniel Lieuwen, Paulo Santos, and Haobo Ren
Page(s): 72-84
Full Text:
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The convergence of the wireline telecom, wireless telecom, and internet networks and the services they
provide offers tremendous opportunities in services personalization. We distinguish between two broad
categories of personalization systems: recommendation systems, such as used in advertising, and life-
style assisting systems, which attempt to customize or specialize services to an individual’s needs,
preferences, and habits. The Privacy-Conscious Personalization (PCP) framework, developed previously at
Bell Labs, uses a high-speed rules engine to enable rich life-style assisting personalization. During
network-hosted information sharing and call processing, the PCP framework can be used to interpret a
combination of incoming requests, user data, and user preferences in order to provide contextaware,
requester-targeted, and preferences-driven responses to those requests (e.g., deciding whether to share a
user’s location with a given requester, what to show as the enduser’s availability to a given requester,
where to forward an incoming call). This paper describes key aspects of a new initiative at Bell Labs, called
Intuitive Network Applications (INA), which aims to combine human factors and automated learning
techniques, in order to gather the user data and preferences needed for PCP-enabled personalization, with
minimal disruption to the user. A particular focus of the paper is on life-style assisting capabilities for
applications that involve the interaction of an end-user with her social network, i.e., family, friends,
colleagues, customers, etc. The paper describes (i) key requirements, (ii) a high-level architectural
framework, and (iii) some specific directions currently under exploration for filling out the framework.

Index Terms
context, converged services, learning, personalization, preferences, ubiquitous computing