So, I've been thinking about this a bit more. Take an example: I haverated a very small number of items. I am able to extract a neighborhood of similar users. Now let's say there is a single user who has rated the same items with the same rating, but this user is the only rater in my neighborhood who has rated an obscure item very highly. In the case using a weighted average to predict my recommendations, this obscure item would rise to the top of the list. In this case, it seems like items rated the most would be better recommendations.
I was able to hijack the GenericUserRecommender and change the calculation of the preference to return the count rather than the weighted average. In my case, this seems to return more intuitive results. Again this is related to the sparseness of the data, but I could see this type of thing occurring often. Any thoughts? On Feb 18, 2011, at 3:43 PM, Chris Schilling wrote: > Hello again, > > Very simple question here: I am also testing the user-user cf in mahout. > So, once I define my user neighborhood, is it possible to select the > recommendations from that based on the number of preferences per item rather > than a weighted average? Basically, I'd like to recommend the items with the > most preferences. It would be simple to implement, so I was curious if this > was already possible. I understand that in this case, the counts become > dependent on the size of the neighborhood. This is something I'd want to use > for testing. > > Thanks > Chris
