A collaborative situation-aware scheme for mobile service recommendation

Abstract

Situation-aware service recommendation for mobile devices is aimed at proactively pushing personalized suggestions to users, presenting them unseen or unknown services. A challenging area in the field is that of recommendation schemes emerging from users' collective behavior. When we consider a mobile user, for instance, the recommendation process can be based on social events that can arise from collective positioning information. In this scenario, we discuss a collaborative multi-agent scheme for event detection, in which fuzzy representations are employed to cope with the approximation typical of implicit and aggregated information. More specifically, the first level of information processing is managed by marking agents leaving marks in the environment which are associated with users' positioning. The accumulation of marks enables a fuzzy information granulation process, managed by event agents, in which relevant events can emerge. Finally, a fuzzy inference level, managed by situation agents, deduces user situations from the underlying events.