====== Introduction ======
Ranking is an important concept in modelling our preferences. 
We rank hotels by their quality using one star to five stars;
we rank baseball teams by their records using pairwise competitions;
we rank job applicants by their ability using ordered scores.
In machine learning, the ranking concept
corresponds to a rich family of important problems,
which lend themselves to
a wide range of applications from social science to 
behavioural science to information retrieval. 
For instance, in a Web search system, 
we want the machines to automatically
rank/order the results of our query based on relevance;
in an online shopping system,
we want the machines to automatically
rank/rate the products based on user evaluations;
in a music playing system,
we want the machines to automatically
rank/recommend the songs based on our personal tastes.

====== Our Related Works ======

===== paper =====
  * Yu-Xun Ruan, Hsuan-Tien Lin and Ming-Feng Tsai, Improving Ranking Performance with Cost-sensitive Ordinal Classification via Regression,  Improving Ranking Performance with Cost-sensitive Ordinal Classification via Regression. Information Retrieval, 2013. [[http://www.csie.ntu.edu.tw/~htlin/paper/doc/cocr.pdf|link]]
  * Hsuan-Tien Lin and Ling Li. Reduction from Cost-sensitive Ordinal Ranking to Weighted Binary Classification. Neural Computation, to appear. Some preliminary parts appeared in NIPS '06 and PL Workshop @ ECML/PKDD '09. [[http://www.csie.ntu.edu.tw/~htlin/paper/doc/redordinal.pdf|link]]
  * Ming-Feng Tsai, Shang-Tse Chen, Yao-Nan Chen, Chun-Sung Ferng, Chia-Hsuan Wang, Tzay-Yeu Wen and Hsuan-Tien Lin. An Ensemble Ranking Solution to the Yahoo! Learning to Rank Challenge. National Taiwan University, Technical Report, September 2010. [[http://www.csie.ntu.edu.tw/~htlin/paper/doc/wsltr10ensemble.pdf|link]]
  * Hsuan-Tien Lin and Ling Li. Combining Ordinal Preferences by Boosting. Preference Learning Workshop @ ECML/PKDD '09, 2009. [[http://www.csie.ntu.edu.tw/~htlin/paper/doc/wspl09adaboostor.pdf|link]]

===== thesis =====
  * Yu-Xun Ruan. Studies on Ordinal Ranking with Regression. Master's Thesis, 2010.
  * Ken-Yi Lin. Data Selection Techniques for Large-scale RankSVM. Master's Thesis, 2009.