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.

• 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. 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. 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. link
• Hsuan-Tien Lin and Ling Li. Combining Ordinal Preferences by Boosting. Preference Learning Workshop @ ECML/PKDD '09, 2009. link

• 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.