KRAMER: New Social Medium Based on Collaborative Recognition of Important Situations

In modern societies the process of communication is greatly influenced by information technology and computer systems. Social interactions in both real-life and cyber communities are frequently being shaped by two main features of social computing tools: (1) sharing great deal of information with whole groups of consumers, and (2) deriving collective intelligence by collaborative information evaluation, discussion, annotation, etc. The latter is further supported by reasoning mechanisms implemented in software to derive more pertinent and synthesized information for its consumers, e.g. recommendations. In consequence, communities are empowered to make more than ever informed conclusions and decisions. In our work we consider situations that people find themselves in, as pieces of information frequently driving decision making in classical human relations. We argue that augmenting social intelligence can be achieved by both (1) facilitating sharing context among community members, and (2) encouraging their collaborative effort to learn about importance of certain situations. We present KRAMER, a recommender system that enriches social computing principle with that notion of situation-awareness. In this paper we discuss our system putting stress on its social computing mechanisms. We present also its evaluation in a form of a special user test-game.