A weight-aware recommendation algorithm for mobile multimedia systems
Mobile Information Systems
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Today smart phones integrate many sensors and provide large computing capacities. They enable the shift towards massive quantities of real-time information becoming access push rather than demand pull on a global case. In this we describe CAPIM, a platform to support such a paradigm. It integrates services to monitor and a context for adapt with the user's context using sensors and capabilities of smart phones, together with online social data. It integrates context-aware services that are dynamically configurable and use the user's location, identity, preferences, profile, and relations with individuals, as well as capabilities of the mobile devices to manifest themselves in many different ways and re-invent themselves over and over again. Such services aggregate and semantically organize the context data. They react based on dynamically defined context-oriented workflows, and the platform includes an execution engine that supports context-aware actions for orientation, information, and recommendation. We describe the design of our system, the challenges that need to be solved, and the evaluation methodology we are planning to adopt.