org.apache.mahout.cf.taste.neighborhood.UserNeighborhood Java Examples
The following examples show how to use
org.apache.mahout.cf.taste.neighborhood.UserNeighborhood.
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Example #1
Source File: MovieUserRecommender.java From hiped2 with Apache License 2.0 | 6 votes |
private static void recommend(String ratingsFile, int ... userIds) throws TasteException, IOException { DataModel model = new FileDataModel(new File(ratingsFile)); UserSimilarity similarity = new PearsonCorrelationSimilarity(model); UserNeighborhood neighborhood = new NearestNUserNeighborhood( 100, similarity, model); Recommender recommender = new GenericUserBasedRecommender( model, neighborhood, similarity); Recommender cachingRecommender = new CachingRecommender(recommender); for(int userId: userIds) { System.out.println("UserID " + userId); List<RecommendedItem> recommendations = cachingRecommender.recommend(userId, 2); for(RecommendedItem item: recommendations) { System.out.println(" item " + item.getItemID() + " score " + item.getValue()); } } }
Example #2
Source File: UserbasedRecommender.java From Building-Recommendation-Engines with MIT License | 5 votes |
public static void main( String[] args ) throws IOException, TasteException { //user based recommender model DataModel model = new FileDataModel(new File("data/dataset.csv")); UserSimilarity similarity = new PearsonCorrelationSimilarity(model); UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model); UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity); List<RecommendedItem> recommendations = recommender.recommend(2, 3); for (RecommendedItem recommendation : recommendations) { System.out.println(recommendation); } }
Example #3
Source File: MovieUserEvaluator.java From hiped2 with Apache License 2.0 | 5 votes |
@Override public Recommender buildRecommender(DataModel model) throws TasteException { UserSimilarity similarity = new PearsonCorrelationSimilarity(model); UserNeighborhood neighborhood = new NearestNUserNeighborhood( 100, similarity, model); return new GenericUserBasedRecommender( model, neighborhood, similarity); }