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From charlysf <>
Subject Re: Questions about PearsonCorrelation on a example
Date Tue, 23 Jun 2009 22:52:15 GMT

This recommendation is my explicit basis recommendation, for the implicit, I
don't have this problem, so I will start to recommend based on the
implicit... and store more explicit data and after to merge the both.

Now, I have a similarity based on implicit & explicit ratings, and I would
like to make some clusters of users, which algorithm do you recommand to me
I would like to do the same after for items, and to have some
recommendations based on clusters.

srowen wrote:
> One could argue that this behavior is actually a good thing -- basing
> an estimate of similarity based on one data point could be very
> unreliable.
> There are some similarity metrics that don't have this property, but
> they all basically ignore the rating value. See
> LogLikelihoodSimilarity or TanimotoCoefficientSimilarity for instance.
> You could try them, but, if you have ratings I would think you want to
> start by using them. In that case, I'd suggest you make up some more
> data instead!
> On Tue, Jun 23, 2009 at 6:43 PM, charlysf<> wrote:
>> Of course, thank you very much, it's because now my table is almost
>> empty, in
>> this case, do you recommend to me to use an other similarity ?

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