Won't argue with how fast Solr is, It's another fast and scalable lookup engine and another option. Especially if you don't need to lookup anything else by user, in which case you are back to a db...
Using a cooccurrence matrix means you are doing item similairty since there is no user data in the matrix. Or are you talking about using the user history as the query? in which case you have to remember somewhere all users' history and look it up for the query, no? On May 19, 2013, at 8:09 PM, Ted Dunning <ted.dunn...@gmail.com> wrote: On Sun, May 19, 2013 at 8:04 PM, Pat Ferrel <p...@occamsmachete.com> wrote: > Two basic solutions to this are: factorize (reduces 100s of thousands of > items to hundreds of 'features') and continue to calculate recs at runtime, > which you have to do with Myrrix since mahout does not have an in-memory > ALS impl, or move to the mahout hadoop recommenders and pre-calculate recs. > Or sparsify the cooccurrence matrix and run recommendations out of a search engine. This will scale to thousands or tens of thousands of recommendations per second against 10's of millions of items. The number of users doesn't matter.
