org.apache.mahout.cf.taste.impl.recommender.knn
Class KnnItemBasedRecommender
java.lang.Object
org.apache.mahout.cf.taste.impl.recommender.AbstractRecommender
org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender
org.apache.mahout.cf.taste.impl.recommender.knn.KnnItemBasedRecommender
- All Implemented Interfaces:
- Refreshable, ItemBasedRecommender, Recommender
public final class KnnItemBasedRecommender
- extends GenericItemBasedRecommender
The weights to compute the final predicted preferences are calculated using linear interpolation, through
an Optimizer
. This algorithm is based in the paper of Robert M. Bell and Yehuda Koren in ICDM '07.
Methods inherited from class org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender |
estimatePreference, getDefaultMostSimilarItemsCandidateItemsStrategy, getSimilarity, mostSimilarItems, mostSimilarItems, mostSimilarItems, mostSimilarItems, mostSimilarItems, mostSimilarItems, recommend, recommendedBecause, refresh, toString |
KnnItemBasedRecommender
public KnnItemBasedRecommender(DataModel dataModel,
ItemSimilarity similarity,
Optimizer optimizer,
CandidateItemsStrategy candidateItemsStrategy,
MostSimilarItemsCandidateItemsStrategy mostSimilarItemsCandidateItemsStrategy,
int neighborhoodSize)
KnnItemBasedRecommender
public KnnItemBasedRecommender(DataModel dataModel,
ItemSimilarity similarity,
Optimizer optimizer,
int neighborhoodSize)
doEstimatePreference
protected float doEstimatePreference(long theUserID,
PreferenceArray preferencesFromUser,
long itemID)
throws TasteException
- Overrides:
doEstimatePreference
in class GenericItemBasedRecommender
- Throws:
TasteException
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