Yup, actually that will be the most natural way to incorporate it into my code, which is set up for the more general case of a spectrum of user ratings, and a range of item-item similarities, so by default it will do what you suggest here. Good good.
... and the rest, I admit, is outside the scope of my knowledge. The whole "model-based" recommender thing is conspicuously missing from the package. I'm encouraged by this book "Collective Intelligence" which explained much of this to me at a basic level, so I can probably finally cook up a naive Bayesian classifier-based recommender at some point. ... and then understand all the better ideas you are suggesting. On Sat, May 31, 2008 at 2:32 PM, Ted Dunning <[EMAIL PROTECTED]> wrote: > Yes. That is a good basic recommendation system. Another approach is to > use the co-occurrence matrix to find items that have anomalous co-occurrence > and then build a weighted model based on overall frequency. This allows you > to weight the recommendations differently than you would with the raw > co-occurrence score. If you have the right audience and interface then you > will still do quite well even with some moderately poor ordering of the > recommendations because your viewers will dig pretty far down into the > list. Some other interfaces are not so forgiving (think radio). > > > Variants on that include finding a latent variable representation of movies > and people that explains which movies people have seen. The movies you have > seen will define a latent variable representation for you and that should > allow you to determine which movies you should have seen. This general > approach subsumes LSI, pLSI, LDA, MDCA and non-negative matrix factorization > for different definitions of latent variable structure. It would be nice to > be able to distinguish moves that you have not seen because you never heard > of them from movies that you declined to see, but in many domains where > marketing is not such a strong effect, you can presume that all things that > you have not consumed are things you know nothing about. For movies, this > is a weak approximation, for music it is slightly better, for user generated > content it is very accurate.
