Hi All,
I am using the Mahout to build a user-based recommender system (RS). The
evaluation method I am using is
AverageAbsoluteDifferenceRecommenderEvaluator, which according to the
"Mahout in Action" randomly sets aside some existing preference and
calculate the difference between the predi
e to Mahout, it is going to give me a really nice
result because most of the preference is 1. So is there any other metric
to evaluate this?
Any help will be highly appreciated.
Best Regards,
Jimmy
Zhongduo Lin (Jimmy)
MASc candidate in ECE department
University of Toronto
On 2013-05-08
do one of
those two things.
Try mean average precision for a metric that is not directly related
to the prediction values.
On Wed, May 8, 2013 at 2:45 PM, Zhongduo Lin wrote:
Thank you for your reply.
I think the evaluation process involves randomly choosing the evaluation
proportion. The
can't predict item 4 in that case. that shows the weakness of
neighborhood approaches for sparse data. That's pretty much the story
-- it's all working correctly. Maybe you should not use this approach.
On Wed, May 8, 2013 at 4:00 PM, Zhongduo Lin wrote:
Thank you for the quick res
m so as to not favor one algorithm.
I think you are just arguing that the algorithm you are using isn't
good for your data -- so just don't use it. Is that not the answer?
I don't know what you mean by the mapping algorithm.
On Wed, May 8, 2013 at 4:17 PM, Zhongduo Lin wrote:
Thank
This accounts for why a neighborhood size of 2 always gives me the best
result. Thank you!
Best Regards,
Jimmy
Zhongduo Lin (Jimmy)
MASc candidate in ECE department
University of Toronto
On 2013-05-08 2:40 PM, Alejandro Bellogin Kouki wrote:
AFAIK, the recommender would predict a NaN, which
I see. Thank you for your information! Any idea about evaluating the
method of mapping inferred preference to a smaller scale with Mahout?
Best Regards,
Jimmy
Zhongduo Lin (Jimmy)
MASc candidate in ECE department
University of Toronto
On 2013-05-08 3:32 PM, Sean Owen wrote:
Ah, yes that&#
