Hi,
I was wondering how can we handle new data on existing users .Do we have
to run Matrix Factorization job on all users and items again every time a
new/item is preferred by an existing user? Or we can take the new data of
existing user perform user rating and run recommender job using previously
computed user-features and item-feature matrix?
Reason I’m asking this is me and my team are working with transaction data
of 17million customers and close to 170k unique items. We have close to
half a billion records. So the factorization job takes a lot of time.
Ideally we want to recommend items to the users based on their most
recent behavior .The only way to do that is to updated the utility matrix
of a given user vector with his most recent purchase. But if we do that do
I have to re run the factorization job again or there is a way around
this?
