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 are the feature engineering practices that I should 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%
