Thanks Ted, awesome(and intuitive) how you reduced my problem by comparing
features to users!
Mridul
On 30 September 2013 10:47, Ted Dunning ted.dunn...@gmail.com wrote:
Yes. You can turn the normal item-item relationships around to get this.
What you have is an item x feature matrix.
Hi
I have records - items - with many features.
Something like
ID, feature1, feature2, ..., featureN
Can I leverage Mahout's log-likelihood similarity metrics for calculating
the K-Most similar items to a given item X?
-
Thanks
Mridul
Yes. You can turn the normal item-item relationships around to get this.
What you have is an item x feature matrix. Normally, one has a user x item
matrix in cooccurrence analysis and you get an item x item matrix.
If you consider the features to be users in the computation, then the
resulting
