net.librec.recommender.baseline

Class UserClusterRecommender

java.lang.Object

net.librec.recommender.AbstractRecommender

net.librec.recommender.ProbabilisticGraphicalRecommender

net.librec.recommender.baseline.UserClusterRecommender

All Implemented Interfaces:

Recommender

public class UserClusterRecommender
extends ProbabilisticGraphicalRecommender

It is a graphical model that clusters users into K groups for recommendation, see reference: Barbieri et al., Probabilistic Approaches to Recommendations (Section 2.2), Synthesis Lectures on Data Mining and Knowledge Discovery, 2014.

Author:

Guo Guibing and Zhang Haidong

Field Summary

Fields inherited from class net.librec.recommender.ProbabilisticGraphicalRecommender

burnIn, numItems, numIterations, numStats, numUsers, sampleLag

Fields inherited from class net.librec.recommender.AbstractRecommender

conf, context, decay, earlyStop, globalMean, isBoldDriver, isRanking, itemMappingData, LOG, loss, maxRate, minRate, numRates, ratingScale, recommendedList, testMatrix, topN, trainMatrix, userMappingData, validMatrix, verbose

Constructor Summary

Constructors

Constructor and Description
UserClusterRecommender()

Method Summary

Methods

Modifier and Type	Method and Description
protected void	eStep() parameters estimation: used in the training phase
protected boolean	isConverged(int iter) Post each iteration, we do things: print debug information check if converged if not, adjust learning rate
protected void	mStep() update the hyper-parameters
protected double	predict(int userIdx, int itemIdx) predict a specific rating for user userIdx on item itemIdx, note that the prediction is not bounded.
protected void	setup() setup init member method

Methods inherited from class net.librec.recommender.ProbabilisticGraphicalRecommender

estimateParams, readoutParams, trainModel

Methods inherited from class net.librec.recommender.AbstractRecommender

cleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, loadModel, predict, recommend, recommend, recommendRank, recommendRating, saveModel, setContext

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

UserClusterRecommender

public UserClusterRecommender()

Method Detail

setup

protected void setup()
              throws LibrecException

Description copied from class: ProbabilisticGraphicalRecommender

setup init member method

Overrides:

setup in class ProbabilisticGraphicalRecommender

Throws:

LibrecException - if error occurs during setting up

eStep

protected void eStep()

Description copied from class: ProbabilisticGraphicalRecommender

parameters estimation: used in the training phase

Specified by:

eStep in class ProbabilisticGraphicalRecommender

mStep

protected void mStep()

Description copied from class: ProbabilisticGraphicalRecommender

update the hyper-parameters

Specified by:

mStep in class ProbabilisticGraphicalRecommender

isConverged

protected boolean isConverged(int iter)

Description copied from class: AbstractRecommender

Post each iteration, we do things:

1. print debug information
2. check if converged
3. if not, adjust learning rate

Overrides:

isConverged in class ProbabilisticGraphicalRecommender

Parameters:

iter - current iteration

Returns:

boolean: true if it is converged; false otherwise

predict

protected double predict(int userIdx,
             int itemIdx)
                  throws LibrecException

Description copied from class: AbstractRecommender

predict a specific rating for user userIdx on item itemIdx, note that the prediction is not bounded. It is useful for building models with no need to bound predictions.

Specified by:

predict in class AbstractRecommender

Parameters:

userIdx - user index

itemIdx - item index

Returns:

predictive rating for user userIdx on item itemIdx without bound

Throws:

LibrecException - if error occurs during predicting