prea.recommender.matrix
Class BayesianPMF
java.lang.Object
prea.recommender.matrix.MatrixFactorizationRecommender
prea.recommender.matrix.BayesianPMF
- All Implemented Interfaces:
- Recommender
public class BayesianPMF
- extends MatrixFactorizationRecommender
This is a class implementing Bayesian Probabilistic Matrix Factorization.
Technical detail of the algorithm can be found in
Ruslan Salakhutdinov and Andriy Mnih, Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo,
Proceedings of the 25th International Conference on Machine Learning, 2008.
- Since:
- 2012. 4. 20
- Version:
- 1.1
- Author:
- Joonseok Lee
Fields inherited from class prea.recommender.matrix.MatrixFactorizationRecommender |
featureCount, itemCount, itemFeatures, learningRate, maxIter, maxValue, minValue, momentum, offset, regularizer, showProgress, userCount, userFeatures |
Constructor Summary |
BayesianPMF(int uc,
int ic,
double max,
double min,
int fc,
double lr,
double r,
double m,
int iter,
boolean verbose)
Construct a matrix-factorization model with the given data. |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
BayesianPMF
public BayesianPMF(int uc,
int ic,
double max,
double min,
int fc,
double lr,
double r,
double m,
int iter,
boolean verbose)
- Construct a matrix-factorization model with the given data.
- Parameters:
uc
- The number of users in the dataset.ic
- The number of items in the dataset.max
- The maximum rating value in the dataset.min
- The minimum rating value in the dataset.fc
- The number of features used for describing user and item profiles.lr
- Learning rate for gradient-based or iterative optimization.r
- Controlling factor for the degree of regularization.m
- Momentum used in gradient-based or iterative optimization.iter
- The maximum number of iterations.verbose
- Indicating whether to show iteration steps and train error.
buildModel
public void buildModel(SparseMatrix rateMatrix)
- Build a model with given training set.
- Specified by:
buildModel
in interface Recommender
- Overrides:
buildModel
in class MatrixFactorizationRecommender
- Parameters:
rateMatrix
- Training data set.