When evaluating recommender before running evaluator put RandomUtils.useTestSeed();
to make splitting of data set consistent; don't use it in production, just for evaluation. This is all explained more thoroughly in Mahout in Action book. Kind regards, Stevo Slavic. On Wed, Jan 23, 2013 at 2:01 PM, Zia mel <[email protected]> wrote: > Hi > I used NearestNUserNeighborhood with RMSE in a user recommender that > use PearsonCorrelationSimilarity , I found that changing the > neighborhood size has no clear pattern or effect. Sometimes it > increase others decrease. While using the neighborhood size with > precision has a better pattern. Any reason? Another point is that the > RMSE change for every run since it choose different sample , so would > running the code for 10 or 20 times and taking the average be a good > idea or there is better thing to do? > > //-- RUN 1 > 2, 0.5523623146152608 > 3, 0.5425283201773704 > 4, 0.669846658662311 > 5, 0.5956616542334392 > 6, 0.6033911039809353 > 7, 0.6135206544496685 > 8, 0.5740444208649034 > 9, 0.642798288443049 > 10, 0.6266535555651472 > > //-- RUN 2 > 2, 0.5415411343523825 > 3, 0.6784589323396696 > 4, 0.6347069968141124 > 5, 0.6968820296725008 > 6, 0.5953849874479478 > 7, 0.6791828191904128 > 8, 0.6072462830257853 > 9, 0.6461346217476011 > 10, 0.6043919119341171 > > Thanks ! >
