  In a typical Event-Based Surveillance setting, a stream of web documents is
continuously monitored for disease reporting. A structured representation of
the disease reporting events is extracted from the raw text, and the events are
then aggregated to produce signals, which are intended to represent early
warnings against potential public health threats.
  To public health officials, these warnings represent an overwhelming list of
"one-size-fits-all" information for risk assessment. To reduce this overload,
two techniques are proposed. First, filtering signals according to the user's
preferences (e.g., location, disease, symptoms, etc.) helps reduce the
undesired noise. Second, re-ranking the filtered signals, according to an
individual's feedback and annotation, allows a user-specific, prioritized
ranking of the most relevant warnings.
  We introduce an approach that takes into account this two-step process of: 1)
filtering and 2) re-ranking the results of reporting signals. For this,
Collaborative Filtering and Personalization are common techniques used to
support users in dealing with the large amount of information that they face.
