Hello, in the book Mahout in Action there are a few lines of code to evaluate boolean preferences with precision and recall, but i can vary threshold as much as i want, it won't change anything. Only changing "at" (number of recommendations) seems to change the results.
My problem is, that i get P = R = 0.0033 which i would think is *very* low. relevanceThreshold = 1 and at = 10. As far as i can see GenericRecommenderIRStatsEvaluator gets a list of preferences for each user and compares these to some threshold. The BooleanPreference returns always 1 as a value, so this doesn't make much sense to me in this context, since it would return all the elements. One could completely skip this comparison with a Boolean data model, right? Then i makes recommendations based on the training model and then checks which of these recommendations is contained in the list of preferences. Thats sounds reasonable. Better would be to have two DataModels which are divided at a certain point in time and then we make recommendations based on the older one and check if these occur in the newer one, correct? This way we would have a way to tell which ones are "good" recommendations and which one are not. regards Christoph -- Christoph Hermann Institut für Informatik Tel: +49 761-203-8171 Fax: +49 761-203-8162 e-mail: herm...@informatik.uni-freiburg.de
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