A Framework for Learning to Personalize Converged Services Involving Social Networks

The convergence of the wireline telecom, wireless telecom, and Internet networks and the services they provide offers tremendous opportunities in services personalization. The privacy-conscious personalization (PCP) framework, developed previously at Bell Labs, uses a high-speed rules engine during call processing to interpret a combination of incoming requests, user data, and user preferences in order to provide context-aware, 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 end-user'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 combines human factors and automated learning techniques, in order to gather and apply user data and preferences, to enable effective adaptation to end-user needs with minimal disruption to her. The focus of this paper is on applications which 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