Learning From Ordered Sets and Applications in Collaborative Ranking

Truyen Tran, Dinh Phung, Svetha Venkatesh
Proceedings of the Asian Conference on Machine Learning, PMLR 25:427-442, 2012.

Abstract

Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed 5 stars to them, or a customized tour package. It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and ordering the subsets. Here we construct a probabilistic log-linear model over a set of ordered subsets. Inference in this combinatorial space is highly challenging: The space size approaches (N!=2)6:93145^N+1 as N approaches infinity. We propose a split-and-merge Metropolis-Hastings procedure that can explore the state-space efficiently. For discovering hidden aspects in the data, we enrich the model with latent binary variables so that the posteriors can be efficiently evaluated. Finally, we evaluate the proposed model on large-scale collaborative filtering tasks and demonstrate that it is competitive against state-of-the-art methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v25-tran12b, title = {Learning From Ordered Sets and Applications in Collaborative Ranking}, author = {Tran, Truyen and Phung, Dinh and Venkatesh, Svetha}, booktitle = {Proceedings of the Asian Conference on Machine Learning}, pages = {427--442}, year = {2012}, editor = {Hoi, Steven C. H. and Buntine, Wray}, volume = {25}, series = {Proceedings of Machine Learning Research}, address = {Singapore Management University, Singapore}, month = {04--06 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v25/tran12b/tran12b.pdf}, url = {https://proceedings.mlr.press/v25/tran12b.html}, abstract = {Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed 5 stars to them, or a customized tour package. It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and ordering the subsets. Here we construct a probabilistic log-linear model over a set of ordered subsets. Inference in this combinatorial space is highly challenging: The space size approaches (N!=2)6:93145^N+1 as N approaches infinity. We propose a split-and-merge Metropolis-Hastings procedure that can explore the state-space efficiently. For discovering hidden aspects in the data, we enrich the model with latent binary variables so that the posteriors can be efficiently evaluated. Finally, we evaluate the proposed model on large-scale collaborative filtering tasks and demonstrate that it is competitive against state-of-the-art methods.} }
Endnote
%0 Conference Paper %T Learning From Ordered Sets and Applications in Collaborative Ranking %A Truyen Tran %A Dinh Phung %A Svetha Venkatesh %B Proceedings of the Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2012 %E Steven C. H. Hoi %E Wray Buntine %F pmlr-v25-tran12b %I PMLR %P 427--442 %U https://proceedings.mlr.press/v25/tran12b.html %V 25 %X Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed 5 stars to them, or a customized tour package. It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and ordering the subsets. Here we construct a probabilistic log-linear model over a set of ordered subsets. Inference in this combinatorial space is highly challenging: The space size approaches (N!=2)6:93145^N+1 as N approaches infinity. We propose a split-and-merge Metropolis-Hastings procedure that can explore the state-space efficiently. For discovering hidden aspects in the data, we enrich the model with latent binary variables so that the posteriors can be efficiently evaluated. Finally, we evaluate the proposed model on large-scale collaborative filtering tasks and demonstrate that it is competitive against state-of-the-art methods.
RIS
TY - CPAPER TI - Learning From Ordered Sets and Applications in Collaborative Ranking AU - Truyen Tran AU - Dinh Phung AU - Svetha Venkatesh BT - Proceedings of the Asian Conference on Machine Learning DA - 2012/11/17 ED - Steven C. H. Hoi ED - Wray Buntine ID - pmlr-v25-tran12b PB - PMLR DP - Proceedings of Machine Learning Research VL - 25 SP - 427 EP - 442 L1 - http://proceedings.mlr.press/v25/tran12b/tran12b.pdf UR - https://proceedings.mlr.press/v25/tran12b.html AB - Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed 5 stars to them, or a customized tour package. It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and ordering the subsets. Here we construct a probabilistic log-linear model over a set of ordered subsets. Inference in this combinatorial space is highly challenging: The space size approaches (N!=2)6:93145^N+1 as N approaches infinity. We propose a split-and-merge Metropolis-Hastings procedure that can explore the state-space efficiently. For discovering hidden aspects in the data, we enrich the model with latent binary variables so that the posteriors can be efficiently evaluated. Finally, we evaluate the proposed model on large-scale collaborative filtering tasks and demonstrate that it is competitive against state-of-the-art methods. ER -
APA
Tran, T., Phung, D. & Venkatesh, S.. (2012). Learning From Ordered Sets and Applications in Collaborative Ranking. Proceedings of the Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 25:427-442 Available from https://proceedings.mlr.press/v25/tran12b.html.

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