  Many latent (factorized) models have been proposed for recommendation tasks
like collaborative filtering and for ranking tasks like document or image
retrieval and annotation. Common to all those methods is that during inference
the items are scored independently by their similarity to the query in the
latent embedding space. The structure of the ranked list (i.e. considering the
set of items returned as a whole) is not taken into account. This can be a
problem because the set of top predictions can be either too diverse (contain
results that contradict each other) or are not diverse enough. In this paper we
introduce a method for learning latent structured rankings that improves over
existing methods by providing the right blend of predictions at the top of the
ranked list. Particular emphasis is put on making this method scalable.
Empirical results on large scale image annotation and music recommendation
tasks show improvements over existing approaches.
