Potential problems with expanding training set
One problem is that you insert correlated data points. Different days from the same user (may) have some underlying correlations that the days from different users don't. These correlations might be relevant for the classification task, and the model might learn them, instead of learning the relevant features.

This doesn't mean it's a bad thing. Data augmentation is a thing. For example, a common practice when training models such as CNNs on images is to perform data augmentation by rotating/scaling the initial images. 

However, make sure you don't touch the validation data. The data augmentation is performed after the train-validation split. The validation data should be as "raw" as possible. You can test it this way, and see how it impacts the performance of your model.