the second part, which happens at the test time, a set of items that is *personalized* to the coming user is selected. These are items that are computed specifically according to the features of the coming user. In this manner, the computation for each coming user would be reduced to the selection of the second part, which typically constitutes a small portion of the final set of recommended items. The first part can be done offline with a lower frequency. For instance, in a real recommender system, the first part can be computed once every hour, and the second part can be computed specifically for each coming user (or for a class of similar users). While we have mentioned recommendation (or more generally facility location) as a specific example, it is easy to see that this framework can be easily used to reduce computation in other notable applications of submodular optimization. **Contributions.** Our contributions are threefold:

-   We propose a novel discrete meta-learning framework where each task is equivalent to maximizing a set function under some cardinality constraint. Our framework aims at using prior data, i.e., previously visited tasks, to train a proper initial solution set that can be quickly 