Problem:
I'm currently using python-recsys and SVD algorithm to compute recommendations for my users. Computation is rather quick (for now) but I'm wondering how this would behave if we go live. we have around 1 million products stored in Mongodb and are expecting around 100 users for start. I've simulated situations like that, but this random generated data does not actually apply to real cases.
We use Redis for recommendations storage, they're computed every 2 hours in celery tasks and currently are really memory heavy, although I've made my best to optimize them.
Worrying about future I'm planning to use Neo4j for that task although it's pretty hard to find any real life stories of developers using this db for recommendations.
Generally what I'd like to achieve is reasonably working recommendation engine (mahout would be overkill in that case i guess) which is not really memory because we cannot afford many servers.
How would Neo4j play with that problem ? Are there any good python drivers for that db ? Maybe it'd be better to use current Mongodb/Redis solution and tune it a little and not add another db to current stack ? I was also considering usning separate machine for just pure computation of recommendations - but is it a good choice?
