Improving ranking performance with cost-sensitive ordinal classification via regression

Authors:
Yu-Xun Ruan;Hsuan-Tien Lin;Ming-Feng Tsai
Affiliations:
Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan 10617;Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan 10617;Department of Computer Science and Program in Digital Content and Technologies, National Chengchi University, Taipei, Taiwan 11605
Venue:
Information Retrieval
Year:
2014

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Abstract

This paper proposes a novel ranking approach, cost-sensitive ordinal classification via regression (COCR), which respects the discrete nature of ordinal ranks in real-world data sets. In particular, COCR applies a theoretically sound method for reducing an ordinal classification to binary and solves the binary classification sub-tasks with point-wise regression. Furthermore, COCR allows us to specify mis-ranking costs to further improve the ranking performance; this ability is exploited by deriving a corresponding cost for a popular ranking criterion, expected reciprocal rank (ERR). The resulting ERR-tuned COCR boosts the benefits of the efficiency of using point-wise regression and the accuracy of top-rank prediction from the ERR criterion. Evaluations on four large-scale benchmark data sets, i.e., "Yahoo! Learning to Rank Challenge" and "Microsoft Learning to Rank," verify the significant superiority of COCR over commonly used regression approaches.