Best choice of metric for interest-based age classification

I have a dataset which has users (rows) with the list of their interests (IABs), which looks like this

user_id | gender | list of interests
user 1  | male   | games, productivity
user 2  | female | games, lifestyle, design
user 3  | male   | travel, games, messaging
user 4  | male   | messaging, blogging, lifestyle

Since the number of unique interests are few (~500) and the number of rows are high (~67M), what feature engineering practices should I follow to get an ML model score a better accuracy?

P.S.: Simple model with one hot/count hot vectorization yields an accuracy of ~52%