I'm building a model to predict the lifetime value of a client based on the relational data we have on them. The user table has a bunch of one-to-many child tables that might be predictive. Grossly simplified, the child features boil down to things like:
- a list of item categories that they've bought in the past
- a list of the predominant colors in ads they've clicked on
- etc, etc
In each case, the obvious feature comprises a list of ~ 0-10 choices from a categorical variable. I have several of these features, some of which have as many as ~10k discrete values, so one-hot encoding would get very wide, very fast.
Aside: if there a term of art for this kind of "list-of-tags feature" that I'm referring to as "choose many categorical", please tell me.
Question: Is there an dense encoding scheme that works with choose-many categorical features?