If someone told me to build a recommendation engine, I'd basically use a knn-approach. You'd need to define - a similarity measure for users (based on, e.g.,

If someone told me to build a recommendation engine, I'd basically use a knn-approach. You'd need to define - a similarity measure for users (based on, e.g., what they bought and what they looked at) and - you just recommend the top items the k nearest users bought (weighted by distance), possibly subtracted by the items already bought by the user in question and cleaned from explicit items. Of course, the system would start really bad but it would get gradually better. This doesn't sound too complicated nor too simplistic nor "heavy data science". Unfortunately there are no real technical insights in the article justifying why I shouldn't do this, so anyone can tell me why my approach would be bad or good?