@ModelData(value={"isRating","external","trainMatrix"}) public class ExternalRecommender extends AbstractRecommender
NOTE: This approach is not applicable to item recommendation. Thank Marcel Ackermann for bringing this demand to my attention.
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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ExternalRecommender() |
Modifier and Type | Method and Description |
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protected double |
predict(int userIdx,
int itemIdx)
predict a specific rating for user userIdx on item itemIdx.
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protected void |
trainModel()
train Model
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cleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, isConverged, loadModel, predict, recommend, recommend, recommendRank, recommendRating, saveModel, setContext, setup
protected void trainModel() throws LibrecException
AbstractRecommender
trainModel
in class AbstractRecommender
LibrecException
- if error occurs during training modelprotected double predict(int userIdx, int itemIdx) throws LibrecException
predict
in class AbstractRecommender
userIdx
- user indexitemIdx
- item indexLibrecException
- if error occursCopyright © 2017. All Rights Reserved.