  Ranking is a central task in machine learning and information retrieval. In
this task, it is especially important to present the user with a slate of items
that is appealing as a whole. This in turn requires taking into account
interactions between items, since intuitively, placing an item on the slate
affects the decision of which other items should be placed alongside it. In
this work, we propose a sequence-to-sequence model for ranking called
seq2slate. At each step, the model predicts the next `best' item to place on
the slate given the items already selected. The sequential nature of the model
allows complex dependencies between the items to be captured directly in a
flexible and scalable way. We show how to learn the model end-to-end from weak
supervision in the form of easily obtained click-through data. We further
demonstrate the usefulness of our approach in experiments on standard ranking
benchmarks as well as in a real-world recommendation system.
