# Classification with different types of data

I'm wondering if there is any methodology or work that illustrates learning on different types of data, but with the same goal/prediction task.

As an example, if I have two different data sources: text and images. Let's also suppose that text and images share some subset of features (e.g. metadata). My task would be to predict if given an example $$i$$, where $$i$$ can be an image or text, whether the user will like example $$i$$.

So as a first thought, I could just train two different models, one for text and one for images, but would there be some better approach where we can use information from both?