>"A learning approach to personalized information filtering" by Beerud Dilip >Sheth (<http://citeseer.nj.nec.com/sheth94learning.html>) is his MSc thesis >where he uses genetic algorithms to optimize a set of vectors for >information filtering. > >My own thesis will be on this subject as well. The problem is that these >things try to solve another information filtering problem; given a huge >stream of articles, find ones that are interesting to the user. Restraints >aren't as tight; most of the articles indicated as interesting should be, >and most of those should be found, but it's no big problem if there are some >mistakes - the stream is huge, and even the stream of interesting articles >will often be too much to read anyway. Sorry for joining the thread late, I was off. We have someone here at Logilab working on similar topics to use them in Narval recipes. Currently, we are using classification based on number of occurence of words in web pages to help internet bookmarks classification. The same technique can be easily adapted for emails, or to advise the user on which pages could be intersting for him. For more information on the Narval project, check http://www.logilab.org/narval/ Alexandre Fayolle -- http://www.logilab.com Narval is the first software agent available as free software (GPL). LOGILAB, Paris (France).
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