# User actions sequence classification

I have a training set where each row is a series of user actions on a website (logged in, sent an invoice, etc.) and times deltas in ms between these actions. Each row has a label — a corresponding user class (10 classes):

action132, 2340, action21, 300, ...  3
action238, 1240, action22, 350, ...  6
...
action763, 1240, action42, 750, ...  2


Say, I have 500 unique action types and each row has a variable length. Max sequence length is 100k and there are 10k rows in the training set (10k users).

I need to predict labels for the test set.

If I had only the action sequence, it would be a more understandable task similar to DNA sequence classification of text classification which are normally solved with LSTM/CNN models or transformers. But in my problem these time deltas are good indicators of user behavior and I'd like to make use of these features. Also, the action order is important as well.

What would be an efficient model for this problem?