On Mon, Nov 22, 2010 at 11:46 PM, Dan Brickley <[email protected]> wrote: > 2010/11/22 Fernando Fernández <[email protected]>: >> Lance, >> >> Columns of U are in some contexts called "latent factors". For example, if >> we are applying SVD over a Document(User)-Term(Items) matrix, Columns of U >> could be interpreted as a representation of groups of terms (words that have >> similar meaning or tend to appear together in documents of the same kind, so >> in this case this "latent" factors are "topics" in some way. Another example >> of this is when we apply the SVD factorization in the famous movie >> recommendation problem. The "latent" factors (columns of the U matrix) >> represent somewhat some kind of "movie topics" (Drama, terror, comedy, and >> possible combinations of these...). Note that if we are trying to make >> recommendations of movies, we will recommend movies that has a similar >> topic, i.e. we will recommend probably a whole topic, not an specific >> movie... but SVD helps us find what movies fall into that topic. Note that >> this "topic" could be in fact something more abstract than "Drama" or >> "comedy". > > Naming these seems a fun project; the examples in > http://www.timelydevelopment.com/demos/NetflixPrize.aspx made me > smile... but also illustrate the point. > > 'Offbeat / Dark-Comedy' vs 'Mass-Market / 'Beniffer' Movies'; 'Good' > vs 'Twisted'; 'What a 10 year old boy would watch' vs 'What a liberal > woman would watch'... > > cheers, > > Dan >
It's been done: http://www.nytimes.com/interactive/2010/01/10/nyregion/20100110-netflix-map.html Just wonderful- matching your local zip codes to rentals is a hoot. -- Lance Norskog [email protected]
