Item based recommender can be cached, so if you are recommending similar items based current item being looked at/purchased, it would just be a database lookup. For an SVD based recommender to compute similar items for 1M items with 50 ~ 100 eigenvectors should take ~5-6 hours on similar machine. You can generate a new model every few days and update database of similar items.
2010/8/29 Young <[email protected]> > Hi all, > > Based on 1M dataset, about how many requests could be expected to be > handled at a time when using item-based recommender if the engine runs on a > Core2 2.4G CPU and 4G meomory. > > Thank you very much. > > -- Young > > -- Akshay Uday Bhat. Graduate Student, Computer Science, Cornell University Website: http://www.akshaybhat.com
