I'm actually mocking up a recommendation engine at the moment, and this certainly gives me a lot to think about. For example, I should probably be planning for

I'm actually mocking up a recommendation engine at the moment, and this certainly gives me a lot to think about. For example, I should probably be planning for more future dimensions (ratings, metadata, etc.) so I can manipulate their interactions in a much more elegant way as we add new data, rather than trying to manually flatten everything out (combining metadata and ratings into a score for each item, then calculating compatibility) for every possible combination, which seems like a much less computationally expensive approach. I probably just need to always keep every scrap of data we get, then find the right ways to combine the data into a score with one routine, and compare the scores to calculate compatibility with another. The hard part is figuring out how the score should be calculated (I'll get to that in the morning). Seems like this rabbit hole goes pretty deep...