LSTM Multivariate time series forecasting with multiple inputs for each time step
The only possible way is to create a dataset separately for each user; in the end, if you have 10 users, then you would have 10 different unrelated time series in the same .csv, since each user can exhibit specific characteristics. Evidently we cannot expect to throw 10 different unrelated time series into an LSTM and expect decent results.

The solution to this is to extract the users with the most entries (you could start with the user with the most entries) and apply in the first instance a simpler algorithm, not necessarily an ml-based one but a statistical one, such as VAR.