Thanks. 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<[email protected]> 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 ? > > -- View this message in context: http://www.nabble.com/Questions-about-PearsonCorrelation-on-a-example-tp24175732p24175915.html Sent from the Mahout User List mailing list archive at Nabble.com.
