How to prepare data for LSTM when using multiple time series of different lengths and multiple features?

I have a dataset from a number of users (nUsers). Each user is sampled randomly in time (non-constant nSamples for each user). Each sample has a number of features (nFeatures). For example:

nUsers = 3 ---> 3 users

nSamples = [32, 52, 21] ---> first user was sampled 32 times second user was sampled 52 times etc.

nFeatures = 10 ---> constant number of features for each sample.

I would like the LSTM to produce a current prediction based on the current features and on previous predictions of the same user. Can I do that in Keras using LSTM layer? I have 2 problems:

1. The data has a different time series for each user. How do I incorporate this?
2. How do I deal with adding the previous predictions into the current time feature space in order to make a current prediction?

Thanks for your help!