Hi, Thanks to all for immediate responses. I have tested my binary recommender on one million dataset by dividing it into 80 % train , 20 % test data-set and i observe an average precision value as 0.22 (i.e out of 20 recommendations produced by the recommender ,there are around 5 matches with items in the test data-set) and average recall value to be 0.0135 for my recommendations. I would like to know the quality of these recommendations on the given precision and recall values ? how to estimate the quality of a recommender on the values of precision and recall and how much better could it be improved practically ?
Thanks, svpranay. On Fri, Jun 11, 2010 at 6:00 PM, pranay venkata <svpra...@gmail.com> wrote: > Hi, > > I'm a newbie to mahout.My aim is to produce recommendations on binary user > purchased data.So i applied item-item similarity model in computing top N > recommendations for movie lens data assuming 1-3 ratings as a 0 and 4-5 > ratings as a 1.Then i tried evaluating my recommendations with the ratings > in the test-data but hardly there have been two or three matches from my top > 20 recommendations to the top rated items in test data and no match for most > users. > > So are my recommendations totally bad by nature or do i need to go for a > different measure for evaluating my recommendations ? > > Please help me ! Thanks in advance. > > Pranay, 2nd yr ,UG student. > > -- regards svpranay
