Google Prediction API vs Graph Databases for Generated Recommendations?

Question

(I admit I am no expert in graph databases or NoSQL, having only used it for a few hobby projects so far.)

I've been using technologies like InfiniteGraph and Stig for recommendations - these are graph databases that supposedly are optimized for tasks like this. It looks like the new Google Predictions API is capable of serving the same purpose -- given a data set and a user's actual likes as a subset, be able to predict what the user might actually like.

Is there a sure-metric to compare Google Predictions with other graph-based databases?

Answer 1

The prediction is quite obvious and right. But as per my knowledge, Google Prediction API uses Page ranking mechanism; not sure about graph database. Unlike Facebook, Google might using GDB for Google+, but in one of official neo4j blog they haven't mentioned anything about Google.