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From Sean Owen <sro...@gmail.com>
Subject Re: RecommenderJob in mahout-0.4 returning 1.0 score for each recommendation
Date Fri, 26 Nov 2010 18:50:57 GMT
The behavior difference is fairly simple. Instead of a weighted
average of preferences (which will always equal 1.0), compute some
other function of those weights -- for example, the average of the
weights.

See GenericBooleanPrefItemBasedRecommender. It's actually just summing
the weights. This is nearly the same thing since the number of items
participating in the average is the same for all estimates. *Nearly*
the same since some can be NaN.

It's an open question whether there aren't better functions of the
weights to use, but this is a fine start, IMHO.


On Fri, Nov 26, 2010 at 6:45 PM, Sebastian Schelter <ssc@apache.org> wrote:
> Hi Sean,
>
> the prediction computation for boolean data is done in
> AggregateAndRecommendReducer.reduceBooleanData()
>
> It computes *all* possible items to recommend for the current user and
> writes out only the n first after that, with n being the number
> specified in the parameter --numRecommendations given to RecommenderJob.
>
> Can you point me to the code where the non-distributed code handles the
> problem of ranking them? We could certainly emulate that behaviour in
> the distributed code too.
>
> --sebastian
>
>
>
> Am 26.11.2010 19:35, schrieb Sean Owen:
>> But is it then ranking the recommendations by the estimated pref? If
>> it's always 1, then the ordering is not meaningful.
>>
>> Maybe it is, I just haven't looked at your changes in much detail
>> since you made them although it looked broadly correct and proper.
>>
>> On Fri, Nov 26, 2010 at 6:33 PM, Sebastian Schelter <ssc@apache.org> wrote:
>>
>>> If all ratings have value 1 (cause we use boolean data) the result of
>>> the Predicition can also only be 1.
>>>
>
>

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