We present a reduction framework from ordinal ranking to binary classification. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranker from the binary classifier. Based on the framework, we show that a weighted 0/1 loss of the binary classifier upper-bounds the mislabeling cost of the ranker, both error-wise and regret-wise. Our framework allows not only to design good ordinal ranking algorithms based on well-tuned binary classification approaches, but also to derive new generalization bounds for ordinal ranking from known bounds for binary classification. In addition, our framework unifies many existing ordinal ranking algorithms, such as perceptron ranking and support vector ordinal regression. When compared empirically on benchmark data sets, some of our newly designed algorithms enjoy advantages in terms of both training speed and generalization performance over existing algorithms. In addition, the newly designed algorithms lead to better cost-sensitive ordinal ranking performance as well as improved listwise ranking performance.

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This paper explores relational benefits from Chinese customer's behavioural perspective, which is a valuable addition to the existing literature that focuses on the western cultural background. The findings of this research show that the relational benefits exist in the online shopping context. In addition to confidence benefit, special treatment benefit and social benefit that have been recognized in traditional services, a new benefit scale, honour benefit, is identified in this study. Empirical data from over 500 online consumers suggest that the relational benefits are becoming more and more important and can possibly turn into the sources of the new competitive advantage for the enterprise in the e-business environment.