Future directions in learning to rank

Olivier Chapelle, Yi Chang, Tie-Yan Liu
Proceedings of the Learning to Rank Challenge, PMLR 14:91-100, 2011.

Abstract

The results of the learning to rank challenge showed that the quality of the predictions from the top competitors are very close from each other. This raises a question: is learning to rank a solved problem? On the on hand, it is likely that only small incremental progress can be made in the “core” and traditional problematics of learning to rank. The challenge was set in this standard learning to rank scenario: optimize a ranking measure on a test set. But on the other hand, there are a lot of related questions and settings in learning to rank that have not been yet fully explored. We review some of them in this paper and hope that researchers interested in learning to rank will try to answer these challenging and exciting research questions.

Cite this Paper

BibTeX
@InProceedings{pmlr-v14-chapelle11b, title = {Future directions in learning to rank}, author = {Chapelle, Olivier and Chang, Yi and Liu, Tie-Yan}, booktitle = {Proceedings of the Learning to Rank Challenge}, pages = {91--100}, year = {2011}, editor = {Chapelle, Olivier and Chang, Yi and Liu, Tie-Yan}, volume = {14}, series = {Proceedings of Machine Learning Research}, address = {Haifa, Israel}, month = {25 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v14/chapelle11b/chapelle11b.pdf}, url = {https://proceedings.mlr.press/v14/chapelle11b.html}, abstract = {The results of the learning to rank challenge showed that the quality of the predictions from the top competitors are very close from each other. This raises a question: is learning to rank a solved problem? On the on hand, it is likely that only small incremental progress can be made in the “core” and traditional problematics of learning to rank. The challenge was set in this standard learning to rank scenario: optimize a ranking measure on a test set. But on the other hand, there are a lot of related questions and settings in learning to rank that have not been yet fully explored. We review some of them in this paper and hope that researchers interested in learning to rank will try to answer these challenging and exciting research questions.} }
Endnote
%0 Conference Paper %T Future directions in learning to rank %A Olivier Chapelle %A Yi Chang %A Tie-Yan Liu %B Proceedings of the Learning to Rank Challenge %C Proceedings of Machine Learning Research %D 2011 %E Olivier Chapelle %E Yi Chang %E Tie-Yan Liu %F pmlr-v14-chapelle11b %I PMLR %P 91--100 %U https://proceedings.mlr.press/v14/chapelle11b.html %V 14 %X The results of the learning to rank challenge showed that the quality of the predictions from the top competitors are very close from each other. This raises a question: is learning to rank a solved problem? On the on hand, it is likely that only small incremental progress can be made in the “core” and traditional problematics of learning to rank. The challenge was set in this standard learning to rank scenario: optimize a ranking measure on a test set. But on the other hand, there are a lot of related questions and settings in learning to rank that have not been yet fully explored. We review some of them in this paper and hope that researchers interested in learning to rank will try to answer these challenging and exciting research questions.
RIS
TY - CPAPER TI - Future directions in learning to rank AU - Olivier Chapelle AU - Yi Chang AU - Tie-Yan Liu BT - Proceedings of the Learning to Rank Challenge DA - 2011/01/26 ED - Olivier Chapelle ED - Yi Chang ED - Tie-Yan Liu ID - pmlr-v14-chapelle11b PB - PMLR DP - Proceedings of Machine Learning Research VL - 14 SP - 91 EP - 100 L1 - http://proceedings.mlr.press/v14/chapelle11b/chapelle11b.pdf UR - https://proceedings.mlr.press/v14/chapelle11b.html AB - The results of the learning to rank challenge showed that the quality of the predictions from the top competitors are very close from each other. This raises a question: is learning to rank a solved problem? On the on hand, it is likely that only small incremental progress can be made in the “core” and traditional problematics of learning to rank. The challenge was set in this standard learning to rank scenario: optimize a ranking measure on a test set. But on the other hand, there are a lot of related questions and settings in learning to rank that have not been yet fully explored. We review some of them in this paper and hope that researchers interested in learning to rank will try to answer these challenging and exciting research questions. ER -
APA
Chapelle, O., Chang, Y. & Liu, T.. (2011). Future directions in learning to rank. Proceedings of the Learning to Rank Challenge, in Proceedings of Machine Learning Research 14:91-100 Available from https://proceedings.mlr.press/v14/chapelle11b.html.

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