Learning to rank with a novel kernel perceptron method

While conventional ranking algorithms, such as the PageRank, rely on the web structure to decide the relevancy of a web page, learning to rank seeks a function capable of ordering a set of instances using a supervised learning approach. Learning to rank has gained increasing popularity in information retrieval and machine learning communities. In this paper, we propose a novel nonlinear perceptron method for rank learning. The proposed method is an online algorithm and simple to implement. It introduces a kernel function to map the original feature space into a nonlinear space and employs a perceptron method to minimize the ranking error by avoiding converging to a solution near the decision boundary and alleviating the effect of outliers in the training dataset. Furthermore, unlike existing approaches such as RankSVM and RankBoost, the proposed method is scalable to large datasets for online learning. Experimental results on benchmark corpora show that our approach is more efficient and achieves higher or comparable accuracies in instance ranking than state of the art methods such as FRank, RankSVM and RankBoost.