[ThePhysicist]

Haha yes, I remember seeing washing machines on my landing page for months after I bought one from Amazon. I mean, how many of them could a person need?

Seriously though, I don't understand why it's so hard to take this effect into account, as there should be a very strong negative correlation between a purchase in a given category and the probability of buying an article from that category in the near future, so even a simple ML algorithm should be able to pick this up easily. Anyone here who can explain why this is difficult?

[bcbrown]

The simple algorithm is to build a correlation matrix between of purchases between all items in the store. Then, when given an item to generate recommendations for, you provide the other items with the highest scores, with a "top sellers" correction for the items that are correlated with everything.

I used to work for a company that implemented similar recommendation services. We approached this problem by modelling whether or not a category was likely to have recurring purchases.

[maxerickson]

A pleasing explanation is that it is a book store.

(I'm not saying it is a good or likely explanation)