@ModelData(value={"isRanking","knn","itemMeans","trainMatrix","similarityMatrix"}) public class ItemKNNRecommender extends AbstractRecommender
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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ItemKNNRecommender() |
Modifier and Type | Method and Description |
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void |
createItemSimilarityList()
Create itemSimilarityList.
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double |
predict(int userIdx,
int itemIdx)
(non-Javadoc)
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protected void |
setup()
(non-Javadoc)
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protected void |
trainModel()
(non-Javadoc)
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cleanup, evaluate, evaluateMap, getContext, getDataModel, getRecommendedList, isConverged, loadModel, predict, recommend, recommend, recommendRank, recommendRating, saveModel, setContext
protected void setup() throws LibrecException
setup
in class AbstractRecommender
LibrecException
- if error occurs during setupAbstractRecommender.setup()
protected void trainModel() throws LibrecException
trainModel
in class AbstractRecommender
LibrecException
- if error occurs during training modelAbstractRecommender.trainModel()
public double predict(int userIdx, int itemIdx) throws LibrecException
predict
in class AbstractRecommender
userIdx
- user indexitemIdx
- item indexLibrecException
- if error occurs during predictingAbstractRecommender.predict(int, int)
public void createItemSimilarityList()
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