Consider the recommender as two separate datasets: 1) a "useful number" of similar users as a neighborhood, and 2) a lot of more distant weights summarized and factored in somehow.
The new graph triangle-finder code would find all neighborhoods for all users in one job. On Tue, Aug 16, 2011 at 10:36 PM, Ted Dunning <[email protected]> wrote: > Yes. That is quite reasonably possible. It isn't really micro-sharding > since it will be different for every user rather than being a universal > sharding of all users. > > On Tue, Aug 16, 2011 at 8:35 PM, Lance Norskog <[email protected]> wrote: > >> Are there any recommender algorithms designed for micro-sharding the >> data model? The use case would be a mobile app that stores only a data >> model for the phone owner. >> >> It seems like a user-user recommender does not need data for all >> users; nearby users plus some background noise should be enough to >> achieve good quality recommendations. The entire algorithm could >> create a global dataset, and then pull out a small amount for a given >> user. >> >> -- >> Lance Norskog >> [email protected] >> > -- Lance Norskog [email protected]
