Latent Structured Ranking
Abstract
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.
