For the poly-syllable challenged, hetereoscedasticity - degree of variation changes. This is common with counts because you expect the standard deviation of count data to be proportional to sqrt(n).
time imhogeneity - changes in behavior over time. One way to handle this (roughly) is to first remove variation in personal and item means over time (if using ratings) and then to segment user histories into episodes. By including both short and long episodes you get some repair for changes in personal preference. A great example of how this works/breaks is Christmas music. On December 26th, you want to *stop* recommending this music so it really pays to limit histories at this point. By having an episodic user session that starts around November and runs to Christmas, you can get good recommendations for seasonal songs and not pollute the rest of the universe. On Thu, Mar 27, 2014 at 8:30 AM, j.barrett Strausser < [email protected]> wrote: > For my team it has usually been hetereoscedasticity and time inhomogeneity. > > > > > On Thu, Mar 27, 2014 at 10:18 AM, Tevfik Aytekin > <[email protected]>wrote: > > > Interesting topic, > > Ted, can you give examples of those mathematical assumptions > > under-pinning ALS which are violated by the real world? > > > > On Thu, Mar 27, 2014 at 3:43 PM, Ted Dunning <[email protected]> > > wrote: > > > How can there be any other practical method? Essentially all of the > > > mathematical assumptions under-pinning ALS are violated by the real > > world. > > > Why would any mathematical consideration of the number of features be > > much > > > more than heuristic? > > > > > > That said, you can make an information content argument. You can also > > make > > > the argument that if you take too many features, it doesn't much hurt > so > > > you should always take as many as you can compute. > > > > > > > > > > > > On Thu, Mar 27, 2014 at 6:33 AM, Sebastian Schelter <[email protected]> > > wrote: > > > > > >> Hi, > > >> > > >> does anyone know of a principled approach of choosing the number of > > >> features for ALS (other than cross-validation?) > > >> > > >> --sebastian > > >> > > > > > > -- > > > https://github.com/bearrito > @deepbearrito >
