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
