A context sensitive, yet private experience towards a contextually apt recommendation of service

Abstract

With a growing number of services, a greater demand on end-user time and falling average revenues per user, context sensitive applications can provide the needed enhanced experiences. The question arises as to whether such context sensitive experiences can be delivered while simultaneously addressing the apparently conflicting demand of end-user privacy. To put the problem in perspective a framework to gauge the success of such applications is presented. In the light of a specific experience enhancing application, sensitive to end-user context namely the recommendation of services, we propose an architecture. Also described is a semantic knowledge model for the recommendation of service, a key enabling artifact for the ldquoright service in the right contextrdquo experience. The paper ends with an examination of the feasibility of the proposed solution.