I am having some trouble designing a recommender or predictor system for the following problem.
Consider a system where each user can create custom labels for content. I would like to recommend to each user, which label they might use for new content.
I think it's easiest to explain at the hand of an example, in this case, word tags for email:
User A might label email using their tags:
User B might use completely different labels of their own creation:
Based on the content I would like to predict or recommend to the user their labels most likely suited to their content.
E.g. in the email example, for a new email, recommend to
User B labels:
project based on the content of the email.
A collaborative filtering approach to the problem (using all users and all known tags) would potentially recommend tags that are not the user's own for labeling content, i.e. recommend label
User B, which is not a valid recommendation.
The only idea I have is to build/train a personalized model for each user based on their own data and labels.
Any help is appreciated.