Can I use continuous variables as features. Such as Age, or Doc2Vec vector components.

Nobody has claimed this yet.

I'd like to use for example age as a user feature or the components of e.g. a Doc2Vec as item features. Take the doc2Vec as example. Say I have 5 compoments docvec=[1.2, -1.0, 3.4, -4.9, 0.0] fortthe first item and [2.2, ....] for the second and so on. I can create columns so that my item feature sparse matrix is
item= [
[0, 0, 1.2],
[0, 1, -1.0],
[0, 2, 3.4],
[0, 3, -4.9]
[0,0, 0.0],
[1, 0, 2.2],
[1, 1, ...]
]
The matris is really not sparse, but is anyway represented as such. This works in the sense that fit() gives a result but it takes some time. My question is: Is this supposed to work? the article doesn't say explicitly yes or no. Any help would be appreciated.

No file or test is named. Start by reviewing the article referenced in the issue and the fit() usage for continuous feature matrices; done means the documentation clearly states whether continuous values such as age and Doc2Vec components are supported and how they should be represented.

Written by the indexing model from the issue text.