@ModelData(value={"isRating","biasedMF","userFactors","itemFactors"}) public class MFALSRecommender extends MatrixFactorizationRecommender
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
initMean, initStd, itemFactors, learnRate, maxLearnRate, numFactors, numIterations, regItem, regUser, userFactors
conf, context, decay, earlyStop, globalMean, isBoldDriver, isRanking, itemMappingData, lastLoss, LOG, loss, maxRate, minRate, numItems, numRates, numUsers, ratingScale, recommendedList, testMatrix, topN, trainMatrix, userMappingData, validMatrix, verbose
Constructor and Description |
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MFALSRecommender() |
Modifier and Type | Method and Description |
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protected void |
trainModel()
train Model
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predict, setup, updateLRate
cleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, isConverged, loadModel, predict, recommend, recommend, recommendRank, recommendRating, saveModel, setContext
protected void trainModel() throws LibrecException
AbstractRecommender
trainModel
in class AbstractRecommender
LibrecException
- if error occurs during training modelCopyright © 2017. All Rights Reserved.