Latent Structured Ranking

Jason Weston, John Blitzer
Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, PMLR R10:902-912, 2012.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR10-weston12a, title = {Latent Structured Ranking}, author = {Weston, Jason and Blitzer, John}, booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence}, pages = {902--912}, year = {2012}, editor = {de Freitas, Nando and Murphy, Kevin}, volume = {R10}, series = {Proceedings of Machine Learning Research}, month = {14--18 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r10/main/assets/weston12a/weston12a.pdf}, url = {https://proceedings.mlr.press/r10/weston12a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Latent Structured Ranking %A Jason Weston %A John Blitzer %B Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2012 %E Nando de Freitas %E Kevin Murphy %F pmlr-vR10-weston12a %I PMLR %P 902--912 %U https://proceedings.mlr.press/r10/weston12a.html %V R10 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Weston, J. & Blitzer, J.. (2012). Latent Structured Ranking. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R10:902-912 Available from https://proceedings.mlr.press/r10/weston12a.html. Reissued by PMLR on 04 October 2026.

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