My own preference (pun intended) is to use log-likelihood score for determining which similarities are non-zero and then use simple frequency weighting such as IDF for weighting the similarities. This doesn't make direct use of cooccurrence frequencies, but it works really well. One reason that it seems to work well is that by using only general occurrence frequencies makes it *really* hard to overfit.
On Thu, Nov 15, 2012 at 9:59 AM, Pat Ferrel <[email protected]> wrote: > Using a boolean data model and log likelihood similarity I get > recommendations with strengths. > > If I were using preference rating magnitudes the recommendation strength > is interpreted as the likely magnitude that a user would rate the > recommendation. Using the boolean model I get values approaching 2 (this > over a quick and small sample so not sure of the real range), which leads > me to the question... > > What is the meaning of the strength returned with the recommendation for > boolean data?
