The funny thing is that we're not using the standard approaches to recommendations, though in an introduction to how the systems work in general, it seemed best

The funny thing is that we're not using the standard approaches to recommendations, though in an introduction to how the systems work in general, it seemed best to take the simplest explanation. Part II has something on graph-based approaches, but in general, the class of algorithms we're using tends to, at least in my opinion, produce better results in a couple orders of magnitude less time. I loved Greg's post here: http://cacm.acm.org/blogs/blog-cacm/22925-what-is-a-good-recommendation-algorithm/fulltext http://cacm.acm.org/blogs/blog-cacm/22925-what-is-a-good-rec... It's a valid question to ask, "Will these algorithms really suss out my personal taste?" And I think the answer is, a lot of the time, they'll get close enough to be interesting. When we started Directed Edge, one of the tipping point moments was seeing how bad some of the related films shown in IMDB for my favorite film were. It was a real watershed moment this week when I was running our algorithm against the Netflix dataset after adding 15 of my own ratings to the system and my two favorite films (which I had not rated) came out at #1 and #5, respectively, and at that, with 300x less computation time than a standard item-to-item recommender requires. (We've got implementations of a lot of standard algorithms that we use for benchmarking our own.)