I want to create content-based recommendation system with Tensorflow Recommenders, but I can’t find any resource about it. There are a few about collaborative filtering. including official tutorial, but can’t find content-based (where you recommend based on item attributes and not users interactions).
As I guess TFRS utilizes two tower model with user embedding and item embedding layers, then compiling it with TFRS retrieval task, but I can’t wrap my head around how I can translate it to item features without other users previous interactions (except the one who I should recommend items to).
I know I’m probably lacking knowledge of several important topics of neural nets, Keras layers, matrix factorization, but TFRS recommender seems to be easy to utilize for content-based filtering.
Please recommend what should I do, or any resources about this topic in particular or some prerequisite topics required for it.
