Hi, I have a pretty neat, non-distributed recommender running here. In doing some math on new user growth rate, I thought it might be wise for me to turn to a distributed approach now, rather than later. Just to make sure I'm on the right track, I calculated 1.5 GB for in-memory prefs. Is that pushing it for a single machine recommender?
Am I right in thinking that the distributed approach is a single algorithm, unlike the many possible choices for non-distributed? Is it possible to inject content-aware logic within a distributed recommender? Would I inject that into the map/reduce phase, when the recommendations are generated? Curious to know if there are any examples out there? Thanks!
