for each task while significantly reducing the computation load. In general, most recommendation tasks can be cast as an instance of this setting[@gabillon2013adaptive; @el2009turning; @yue2011linear]. Consider the task of recommending a set of items, e.g., products, locations, ads, to a set of users. One approach for solving such a problem is to find the subset of items that have the highest score over all the previously-visited users and recommend that subset to a new user. Indeed, this approach leads to a reasonable performance at test time; however, it does not provide a user-specific solution for a new user. Another approach is to find the whole subset at the test time when the new user arrives. In contrast to the previous approach, this scheme leads to a user-specific solution, but at the cost of running a computationally expensive algorithm to select all the elements at the test time.

In our meta-learning framework, the process of selecting set items to be recommended to a new user is done in two parts: In the first part, a set of items is selected offline according to prior experience. These items are the most popular items to the previously-visited users (depending on the context). In 