Exploiting user feedback for online filtering in event-based systems.

Modern large-scale internet applications 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 only content consumers are actually interested in. Filtering is executed by profiling producers and consumers, and matching their profiles as new data is produced. Profiles are built by aggregating feedback submitted by consumers on previously received data.