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Thanks! I'll give this a try. My data is in mongodb using ruby/rails as a web front. Data structure shouldn't be too hard...its just books, users, bookmarks,

Thanks! I'll give this a try. My data is in mongodb using ruby/rails as a web front. Data structure shouldn't be too hard...its just books, users, bookmarks, and tags/genre. All have unique ids (mongo UUIDs for users, books, and basic integer ids for the tags) and simple associations so I guess it couldn't be too hard to import. I'm thinking an offline system would be fine for some experiments. Not sure I need some real-time recommender integrated...but I'm new to this, so I guess I'm still not sure what I need.

reverius42 12y ago
The simplest way to start out would be to import from CSV (assuming it's easy to dump users/books/tags from MongoDB to CSV), use that to train the model, then dump the recommendations from the model back to CSV (or using Python code, write them directly to MongoDB). That would give you an offline system with one-time recommendations. You could then run this in batch, on a schedule, to re-train as you get new tags/bookmarks and update the recommendations periodically.