Exploiting user feedback for online Filtering in event-based systems

Modern large-scale internet applications, like the ubiquitous social networks, represent today a fundamental source of information for millions of users. The larger is the user base, the more difficult it is to control the quality of data that is spread from producers to consumers. This can easily hamper the usability of such systems as the amount of low quality data received by consumers grows uncontrolled. In this paper we propose a novel solution to automatically filter new data injected in event-based systems with the aim of delivering to consumers only content they are actually interested in. Filtering is executed at run-time by first profiling both producers and consumers, and then matching their profiles as new data is produced.