MMMF.java

// Copyright (C) 2014 Guibing Guo
//
// This file is part of LibRec.
//
// LibRec is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
//
// LibRec is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with LibRec. If not, see <http://www.gnu.org/licenses/>.
//

package librec.undefined;

import librec.data.SparseMatrix;
import librec.intf.IterativeRecommender;

/**
 * Maximum Margin Matrix Factorization
 * 
 * <p>
 * This method aims to optimzie the NDCG measure. Original implementation in
 * Matlab: <a
 * href="http://ttic.uchicago.edu/~nati/mmmf/code.html">http://ttic.uchicago
 * .edu/~nati/mmmf/code.html</a>
 * </p>
 * 
 * <p>
 * Related Work:
 * <ul>
 * <li>Srebro et al., Maximum-margin Matrix Factorization, NIPS 2005.</li>
 * <li>Rennie and Srebro, Fast Maximum Margin Matrix Factorization for
 * Collaborative Prediction, ICML 2005.</li>
 * <li>Rennie and Srebro, Loss Functions for Preference Levels: Regression with
 * Discrete Ordered Labels, 2005.</li>
 * <li>Weimer et al., Improving Maximum Margin Matrix Factorization, Machine
 * Learning, 2008.</li>
 * <li>Weimer et al., CoFI^{RANK} - Maximum Margin Matrix Factorization for
 * Collaborative Ranking, NIPS 2008.</li>
 * </ul>
 * </p>
 * 
 * @author guoguibing
 * 
 */
public class MMMF extends IterativeRecommender {

	public MMMF(SparseMatrix trainMatrix, SparseMatrix testMatrix, int fold) {
		super(trainMatrix, testMatrix, fold);

		isRankingPred = true;
	}

}