I would like to create a content recommendation system based on binary click data that also takes views into account.
What content a user has been exposed to, and therefore has the chance to click on, is currently biased by a rule based system that is not always documented. I do have view data (if a user saw the content on their screen, regardless of whether it was clicked.), and am wondering how to take this into account with a traditional matrix factorization recommendation system such as this item-item approach, or if there are other other better options.
Any suggestions for implementation in Python are a bonus!