net.librec.recommender.cf.rating.MFALSRecommender Maven / Gradle / Ivy
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/**
* Copyright (C) 2016 LibRec
*
* 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
* The origin paper: Yunhong Zhou, Dennis Wilkinson, Robert Schreiber and Rong
* Pan. Large-Scale Parallel Collaborative Filtering for the Netflix Prize.
* Proceedings of the 4th international conference on Algorithmic Aspects in
* Information and Management. Shanghai, China pp. 337-348, 2008.
* http://www.hpl.hp.com/personal/Robert_Schreiber/papers/2008%20AAIM%20Netflix/
* netflix_aaim08(submitted).pdf
*
* @author wubin (Email: [email protected])
*/
@ModelData({"isRating", "biasedMF", "userFactors", "itemFactors"})
public class MFALSRecommender extends MatrixFactorizationRecommender {
@Override
protected void trainModel() throws LibrecException {
DenseMatrix identify = BuildEyeMatrix(numFactors);
for (int iter = 1; iter <= numIterations; iter++) {
// fix item matrix M, solve user matrix U
for (int userIdx = 0; userIdx < numUsers; userIdx++) {
// number of items rated by user userIdx
SequentialSparseVector userRatingVec = trainMatrix.row(userIdx);
int numItemOfUser = userRatingVec.size();
DenseMatrix M = new DenseMatrix(numItemOfUser, numFactors);
DenseVector uservector = new VectorBasedDenseVector(numItemOfUser);
int index = 0;
for (Vector.VectorEntry ve : userRatingVec) {
int itemIdx = ve.index();
double rating = ve.get();
M.set(index, itemFactors.row(itemIdx));
uservector.set(index, rating);
index += 1;
}
DenseMatrix A = M.transpose().times(M).plus(identify.times(regUser).times(numItemOfUser));
userFactors.set(userIdx, A.inverse().times(M.transpose().times(uservector)));
}
// fix user matrix U, solve item matrix M
for (int itemIdx = 0; itemIdx < numItems; itemIdx++) {
// latent factor of users that have rated item itemIdx
// number of users rate item j
SequentialSparseVector itemRatingVec = trainMatrix.column(itemIdx);
int numusers = itemRatingVec.size();
DenseMatrix U = new DenseMatrix(numusers, numFactors);
DenseVector itemvector = new VectorBasedDenseVector(numusers);
int index = 0;
for (Vector.VectorEntry ve : itemRatingVec) {
int userIdx = ve.index();
double rating = ve.get();
U.set(index, userFactors.row(userIdx));
itemvector.set(index, rating);
index += 1;
}
DenseMatrix A = U.transpose().times(U).plus(identify.times(regItem).times(numusers));
itemFactors.set(itemIdx, A.inverse().times(U.transpose().times(itemvector)));
}
}
}
protected DenseMatrix BuildEyeMatrix(int numDim) throws LibrecException {
double[][] values = new double[numDim][numDim];
for (int i=0; i