ZeroR as baseline for balanced / imbalanced datasets
You should only undersample the data that is used for training. Test data should represent the true distribution.

Then you have at least two possible baselines. One is ZeroR (your second point), which always predicts the majority class. Given a true distribution of (0) 10% and (1) 90%, this would give you a recall of 100% for (1) and 0% for (0) and 90% accuracy. 

If rows represent the true labels and the columns the predictions, the confusion matrix looks something like this:

$\begin{bmatrix}90 &amp; 0\\10 &amp; 0\end{bmatrix}$

The other would be to flip a coin with probability 90% for class (1) (your first point but without undersampling), which gives you a recall of 90% for (1) and 10% for (0), and 82% accuracy, which is a little more balanced:

$\begin{bmatrix}81 &amp; 9\\9 &amp; 1\end{bmatrix}$

If your classes are strongly imbalanced then recall and accuracy have the same problems as you noted above. F1 might be a better choice, but the best metric depends on your application.