(article author here) Very interesting result. What techniques were you using for CF? You're right that buys:profiles ratio is a challenge, but dealing with a

(article author here) Very interesting result. What techniques were you using for CF? You're right that buys:profiles ratio is a challenge, but dealing with a very sparse matrix like that is exactly what CF is for :) You are spot on using a content-based method to limit the universe of recommendations, and then ordering them via CF. Here's someone else talking about the same approach: https://www.quora.com/Recommendation-Systems-What-is-the-difference-between-item-to-item-collaborative-filtering-and-content-based-filtering/answer/Boaz-Ronkin?srid=tzM https://www.quora.com/Recommendation-Systems-What-is-the-dif... I love the idea of doing substitute identification (and complementary products). I made a pass at that some months ago by using some simple statistics to identify products that are never/rarely purchased together, in spite of their relative popularity. The idea being that no one is purchasing e.g. two tubes of different brands of toothpaste together in the same basket, even though both products are independently popular. The initial test led to mixed results, but I didn't pursue it far enough to know if the results would have ultimately been viable. I'd love to know what you find as you dig in.