Ranking with decision tree

Ranking problems have recently become an important research topic in the joint field of machine learning and information retrieval. This paper presented a new splitting rule that introduces a metric, i.e., an impurity measure, to construct decision trees for ranking tasks. We provided a theoretical basis and some intuitive explanations for the splitting rule. Our approach is also meaningful to collaborative filtering in the sense of dealing with categorical data and selecting relative features. Some experiments were made to illustrate our ranking approach, whose results showed that our algorithm outperforms both perceptron-based ranking and the classification tree algorithms in term of accuracy as well as speed.

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