  We present a collection of algorithms to filter a stream of documents in such
a way that the filtered documents will cover as well as possible the interest
of a person, keeping in mind that, at any given time, the offered documents
should not only be relevant, but should also be diversified, in the sense not
only of avoiding nearly identical documents, but also of covering as well as
possible all the interests of the person. We use a modification of the WEBSOM
algorithm, with limited architectural adaptation, to create a user model (which
we call the "user context" or simply the "context") based on a network of units
laid out in the word space and trained using a collection of documents
representative of the context.
  We introduce the concepts of novelty and coverage. Novelty is related to, but
not identical to, the homonymous information retrieval concept: a document is
novel it it belongs to a semantic area of interest to a person for which no
documents have been seen in the recent past. A group of documents has coverage
to the extent to which it is a good representation of all the interests of a
person.
  In order to increase coverage, we introduce an "interest" (or "urgency")
factor for each unit of the user model, modulated by the scores of the incoming
documents: the interest of a unit is decreased drastically when a document
arrives that belongs to its semantic area and slowly recovers its initial value
if no documents from that semantic area are displayed.
  Our tests show that these algorithms can effectively increase the coverage of
the documents that are shown to the user without overly affecting precision.
