Indeed so. Ted Dunning is an Apache Mahout PMC and committer and the whole idea of Search-based Recommenders stems from his work and insights. If u didn't know, the PIO UR uses Apache Mahout under the hood and hence u see the LLR.
On Thu, Nov 16, 2017 at 3:49 PM, Daniel Gabrieli <[email protected]> wrote: > I am pretty sure the LLR stuff in UR is based off of this blog post and > associated paper: > > http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html > > Accurate Methods for the Statistics of Surprise and Coincidence > by Ted Dunning > > http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.5962 > > > On Thu, Nov 16, 2017 at 10:26 AM Noelia Osés Fernández < > [email protected]> wrote: > >> Hi, >> >> I've been trying to understand how the UR algorithm works and I think I >> have a general idea. But I would like to have a *mathematical >> description* of the step in which the LLR comes into play. In the CCO >> presentations I have found it says: >> >> (PtP) compares column to column using >> *log-likelihood based correlation test* >> >> However, I have searched for "log-likelihood based correlation test" in >> google but no joy. All I get are explanations of the likelihood-ratio test >> to compare two models. >> >> I would very much appreciate a math explanation of log-likelihood based >> correlation test. Any pointers to papers or any other literature that >> explains this specifically are much appreciated. >> >> Best regards, >> Noelia >> >
