Latent Structured Ranking
This addresses a specific issue in recommendation and ranking tasks for users needing more coherent or diverse top predictions, but it is incremental as it builds on existing latent models.
The paper tackles the problem of latent factor models ignoring the structure of ranked lists, which can lead to predictions that are either too diverse or not diverse enough, and introduces a scalable method for learning latent structured rankings that improves over existing approaches on large-scale image annotation and music recommendation tasks.
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.