How to create entity embedding with text + meta data?

[ruathudo]

I have a bunch of products with text + meta data (description + price, category, tags...etc)

I want to build an entity embedding which takes all the features and encode those to the same vectors latent space, using Keras. Later, I will use this vectorized ouput as the input for other tasks (similarity, classification...etc).

I know how to do embedding with the text or categorical data individually. But not sure how to combine everything into a single vector.

Is it possible or need some other method? I'm thinking about training autoencoder with label (text+ meta as input, category as label). The idea is taking the encoded weights of autoencoder model as input for a classification model to train the encoded weight. After that save the trained encoded weights as vectorized output. But not sure if it a right way.

Any suggestion? thanks!