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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%

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  • $\begingroup$ Your title suggests a question about metric but your question is asking about feature engineering for fixed metric (accuracy). Please update either the title or question $\endgroup$
    – SajanGohil
    Mar 11, 2023 at 18:03
  • $\begingroup$ @SajanGohil updated the title $\endgroup$
    – theodre7
    Mar 11, 2023 at 18:09
  • $\begingroup$ Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking. $\endgroup$
    – Community Bot
    Mar 13, 2023 at 15:50

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