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]
