TF-IDF uses no click/buys data whatsoever. It's purely based on characteristics of the product (description, name, brand etc etc). Regarding the example pictur

TF-IDF uses no click/buys data whatsoever. It's purely based on characteristics of the product (description, name, brand etc etc). Regarding the example picture I gave: that's the difference between substitutes and complementary products. The example gives alternatives (other straws...). We're building a way to have recommendations on these two types. Why TF-IDF works better vs buys? A set of bought products holds no to little similarities between those products, it's better to use it for complementary products. Why TF-IDF works better vs clicks? Click paths on sites are more random than you think. Combining click data with TF-IDF makes more sense. As said: TF-IDF as a first filter and using CF as sorting could be the most optimal for substitute products. Complementary products should rely more on CF. A problem with clicks/buys only is that new products tend to get overlooked, you can fix that by mixing in TF-IDF.