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Posted to user@mahout.apache.org by Sean Owen <sr...@gmail.com> on 2012/04/13 23:51:14 UTC
Re: Why my evaluator.evaluate return NaN
NaN means "not a number". It is kind of like "null". This means no
answer could be computed, and usually that is because your data is too
sparse. For the kind of recommender you are running, you don't want
such sparse data, and it sounds like you are making the data sparse by
removing a lot of ratings.
On Thu, Apr 12, 2012 at 10:29 PM, <sa...@163.com> wrote:
> I have used the UserBasedRecommender to recommend and used the AverageAbsoluteDifferenceRecommenderEvaluator as evaluator, when the ratings.txt contains more ratings, the evaluator can return some desired results, but when i made some requirement about the ratings.txt, for example, i filtered out those items that receive more than 4 ratings, the evaluator always return NaN. What does NaN mean? It is caused by the similarity metric? I have try to pre calculate users' similarity, but it returns NaN too.
> what is the problem?
Re: Why my evaluator.evaluate return NaN
Posted by Sean Owen <sr...@gmail.com>.
You would have to debug. Similarity is not the only issue. For example
if you select so little test data that none can be chosen, you get the
same result.
(For our purposes here there is only one NaN, and it is one returned
on purpose to mean no answer, rather than an accident of dividing by
zero.)
On Sat, Apr 14, 2012 at 1:26 AM, Dawid Weiss
<da...@cs.put.poznan.pl> wrote:
> Completing Sean's answer, there are many types of NaNs (multiple
> different binary representations) -- check out the wikipedia page
> about it:
>
> http://en.wikipedia.org/wiki/NaN
>
> Typically you'll only care about whether a number is a NaN or not (not
> the kind of NaN).
>
> Dawid
>
> On Fri, Apr 13, 2012 at 11:51 PM, Sean Owen <sr...@gmail.com> wrote:
>> NaN means "not a number". It is kind of like "null". This means no
>> answer could be computed, and usually that is because your data is too
>> sparse. For the kind of recommender you are running, you don't want
>> such sparse data, and it sounds like you are making the data sparse by
>> removing a lot of ratings.
>>
>> On Thu, Apr 12, 2012 at 10:29 PM, <sa...@163.com> wrote:
>>> I have used the UserBasedRecommender to recommend and used the AverageAbsoluteDifferenceRecommenderEvaluator as evaluator, when the ratings.txt contains more ratings, the evaluator can return some desired results, but when i made some requirement about the ratings.txt, for example, i filtered out those items that receive more than 4 ratings, the evaluator always return NaN. What does NaN mean? It is caused by the similarity metric? I have try to pre calculate users' similarity, but it returns NaN too.
>>> what is the problem?
>>
Re: Why my evaluator.evaluate return NaN
Posted by Dawid Weiss <da...@cs.put.poznan.pl>.
Completing Sean's answer, there are many types of NaNs (multiple
different binary representations) -- check out the wikipedia page
about it:
http://en.wikipedia.org/wiki/NaN
Typically you'll only care about whether a number is a NaN or not (not
the kind of NaN).
Dawid
On Fri, Apr 13, 2012 at 11:51 PM, Sean Owen <sr...@gmail.com> wrote:
> NaN means "not a number". It is kind of like "null". This means no
> answer could be computed, and usually that is because your data is too
> sparse. For the kind of recommender you are running, you don't want
> such sparse data, and it sounds like you are making the data sparse by
> removing a lot of ratings.
>
> On Thu, Apr 12, 2012 at 10:29 PM, <sa...@163.com> wrote:
>> I have used the UserBasedRecommender to recommend and used the AverageAbsoluteDifferenceRecommenderEvaluator as evaluator, when the ratings.txt contains more ratings, the evaluator can return some desired results, but when i made some requirement about the ratings.txt, for example, i filtered out those items that receive more than 4 ratings, the evaluator always return NaN. What does NaN mean? It is caused by the similarity metric? I have try to pre calculate users' similarity, but it returns NaN too.
>> what is the problem?
>