Yahoo! Learning to Rank Challenge Overview

Olivier Chapelle, Yi Chang
; Proceedings of the Learning to Rank Challenge, PMLR 14:1-24, 2011.

Abstract

Learning to rank for information retrieval has gained a lot of interest in the recent years but there is a lack for large real-world datasets to benchmark algorithms. That led us to publicly release two datasets used internally at Yahoo! for learning the web search ranking function. To promote these datasets and foster the development of state-of-the-art learning to rank algorithms, we organized the Yahoo! Learning to Rank Challenge in spring 2010. This paper provides an overview and an analysis of this challenge, along with a detailed description of the released datasets.

Cite this Paper


BibTeX
@InProceedings{pmlr-v14-chapelle11a, title = {Yahoo! Learning to Rank Challenge Overview}, author = {Olivier Chapelle and Yi Chang}, booktitle = {Proceedings of the Learning to Rank Challenge}, pages = {1--24}, year = {2011}, editor = {Olivier Chapelle and Yi Chang and Tie-Yan Liu}, volume = {14}, series = {Proceedings of Machine Learning Research}, address = {Haifa, Israel}, month = {25 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v14/chapelle11a/chapelle11a.pdf}, url = {http://proceedings.mlr.press/v14/chapelle11a.html}, abstract = {Learning to rank for information retrieval has gained a lot of interest in the recent years but there is a lack for large real-world datasets to benchmark algorithms. That led us to publicly release two datasets used internally at Yahoo! for learning the web search ranking function. To promote these datasets and foster the development of state-of-the-art learning to rank algorithms, we organized the Yahoo! Learning to Rank Challenge in spring 2010. This paper provides an overview and an analysis of this challenge, along with a detailed description of the released datasets.} }
Endnote
%0 Conference Paper %T Yahoo! Learning to Rank Challenge Overview %A Olivier Chapelle %A Yi Chang %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-chapelle11a %I PMLR %J Proceedings of Machine Learning Research %P 1--24 %U http://proceedings.mlr.press %V 14 %W PMLR %X Learning to rank for information retrieval has gained a lot of interest in the recent years but there is a lack for large real-world datasets to benchmark algorithms. That led us to publicly release two datasets used internally at Yahoo! for learning the web search ranking function. To promote these datasets and foster the development of state-of-the-art learning to rank algorithms, we organized the Yahoo! Learning to Rank Challenge in spring 2010. This paper provides an overview and an analysis of this challenge, along with a detailed description of the released datasets.
RIS
TY - CPAPER TI - Yahoo! Learning to Rank Challenge Overview AU - Olivier Chapelle AU - Yi Chang BT - Proceedings of the Learning to Rank Challenge PY - 2011/01/26 DA - 2011/01/26 ED - Olivier Chapelle ED - Yi Chang ED - Tie-Yan Liu ID - pmlr-v14-chapelle11a PB - PMLR SP - 1 DP - PMLR EP - 24 L1 - http://proceedings.mlr.press/v14/chapelle11a/chapelle11a.pdf UR - http://proceedings.mlr.press/v14/chapelle11a.html AB - Learning to rank for information retrieval has gained a lot of interest in the recent years but there is a lack for large real-world datasets to benchmark algorithms. That led us to publicly release two datasets used internally at Yahoo! for learning the web search ranking function. To promote these datasets and foster the development of state-of-the-art learning to rank algorithms, we organized the Yahoo! Learning to Rank Challenge in spring 2010. This paper provides an overview and an analysis of this challenge, along with a detailed description of the released datasets. ER -
APA
Chapelle, O. & Chang, Y.. (2011). Yahoo! Learning to Rank Challenge Overview. Proceedings of the Learning to Rank Challenge, in PMLR 14:1-24

Related Material