Hi, I was wondering if anybody has dealt with the issue where your recommender system has to deal with a really large number of items which can be recommended, say 10 millions. It would be impractical for the recommender to predict a rating on every single items before ranking them. Can anybody point me to any papers or links for a solution?
This issue also causes some problem for performance tests if we adopt the rank-based measure such as Precision@5. If I want to use this measure Precision@#n to test a recommender system where there are a large number of items to recommend, the likelihood of an item consumed by a user getting into the top #n list should be really low. Any suggestions as to how to handle this case? Thanks, James
