The contingency table is constructed by looking at how many users have expressed preference or interest in two items. If the items are A and B, the pertinent counts are
k11 - the number of users who interacted with both A and B k12 - the number of users who interacted with A but not B k21 - the number of users who interacted with B but not A k22 - the number of users who interacted with neither A nor B. These values are values that go into the contingency table and are all that is needed to compute the LLR value. See http://tdunning.blogspot.de/2008/03/surprise-and-coincidence.html for a detailed description. On Wed, Apr 30, 2014 at 11:31 PM, Mario Levitin <mariolevi...@gmail.com>wrote: > Hi Ted, > I have read the paper. I understand the "Likelihood Ratio for Binomial > Distributions" part. > However, I cannot make a connection with this part and the contingency > table. > > In order to calculate Likelihood Ratio for two Binomial Distributions you > need the values: p, p1, p2, k1, k2, n1, n2. > But the information contained in the contingency table are different from > these values. So, again, I do not understand how the information contained > in the contingency table is linked with Likelihood Ratio for Binomial > Distributions. > > In order to find the similarity between two users I tend to think of the > boolean preferences of user1 as a sample from a binomial distribution and > the boolean preferences of user2 as another sample from a binomial > distribution. Then use the LLR to assess how likely these distributions are > the same. But I don't think this is correct since this calculation does not > use the contingency table. > > I hope my question is clear. > Thanks. > > > > On Mon, Apr 28, 2014 at 2:41 AM, Ted Dunning <ted.dunn...@gmail.com> > wrote: > > > Excellent. Look forward to hearing your reactions. > > > > On Mon, Apr 28, 2014 at 1:14 AM, Mario Levitin <mariolevi...@gmail.com > > >wrote: > > > > > Not yet, but I will. > > > > > > > > > > > Have you read my original paper on the topic of LLR? It explains the > > > > connection with chi^2 measures of association. > > > > > >
