Collaborative Filtering using categorical features
Association rule mining will be helpful here. It calculates the relative likelihood that items will appear together in a user's history. It's different from, and differently useful from, collaborative filtering recommendation techniques.
I am trying to build a recommender system using collaborative filtering. The issues I am facing are :

The User-Item dataset has mostly categorical variables, so cant find the best way to calculate similarity matrix. Euclidean / Cosine distance will not work here, trying with Jaccard distance.
The dataset does not have User rating for items, instead, we have classifiers - "did not buy", "buy", "added to cart but did not buy".

We have used XGB to get the likelihood to buy a particular item by a particular user, but this kind of dataset is not helping for the recommendation.
Can you please suggest any recommendation algorithm (preferably in python) which handles classification and categorical data?
Thanks in advance.