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Learning to Rank with Bayesian Evidence Framework

The problem of ranking has recently gained attention in data learning. The goal ranking is to learn a real-valued ranking function that induces a ranking or ordering over an instance space. In this paper, we apply popular Bayesian techniques on ranking support vector machine. We propose a novel differentiable loss function called trigonometric loss function with the desirable characteristic of natural normalization in the likelihood function, and then set up a Bayesian framework. In this framework, Bayesian inference is used to implement model adaptation, while keeping the merits of ranking SVM. Experimental results on data sets indicate the usefulness of this approach.