To deal with the problem, we propose a novel learning algorithm under the framework of Perceptron, which adopts the ranking model that maximizes marginal relevance at ranking and can optimize any diversity evaluation measure in training. The algorithm, referred to as PAMM (Perceptron Algorithm using Measures as Margins), first constructs positive and negative diverse rankings for each training query, and then repeatedly adjusts the model parameters so that the margins between the positive and negative rankings are maximized. Experimental results on three benchmark datasets show that PAMM significantly outperforms the state-of-the-art baseline methods. can be used to preprocess ClueWeb09 datasets. can be used to initialize original TREC data, e.g. queries, ground truth, negative ranking list. can be used to train the model. python rankFile featureFile resultFile numofPos postiveScore numofNeg negativeScore lamda


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