We address the challenge of transferring human preferences for action selection from simpler source tasks to complex target tasks. Our goal is to enable robots to support humans proactively by predicting their actions — without requiring demonstrations of their preferred action sequences in the target task. Previous research has relied on human experts to design or select a simple source task that can be used to effectively learn and transfer human preferences to a known target. However, identifying such source tasks for new target tasks can demand substantial human effort. Thus, we focus on automating the selection of source tasks, introducing two new metrics. Our first metric selects source tasks in which human preferences can be accurately learned from demonstrations, while our second metric selects source tasks in which the learned preferences, although not as accurate, can match the preferred human actions in the target task. We evaluate our metrics in simulated tasks and two human-led assembly studies. Our results indicate that selecting high-scoring source tasks on either metric improves the accuracy of predicting human actions in the target task. Notably, tasks chosen by our second metric can be simpler than the first, sacrificing learning accuracy but preserving prediction accuracy.