I've been working on something similar - https://github.com/jamii/springer-recommendations

All the open-source engines I've tried scale poorly. Since our input data already contains >200m interactions I suspect recommendify would struggle (from my quick reading it looks like the co-concurrence matrix is stored in redis ie in-memory).

The approach I'm leaning towards at the moment is collating the article->IPs and IP->articles tables in leveldb and then distributing read-only copies to each worker. Everything else can easily be partitioned by article id.