Package org.apache.mahout.cf.taste.similarity

Examples of org.apache.mahout.cf.taste.similarity.UserSimilarity

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    DataModel dataModel = getDataModel();
    User theUser = dataModel.getUser(userID);
    List<User> neighborhood = new ArrayList<User>();
    Iterator<? extends User> users = dataModel.getUsers().iterator();
    UserSimilarity userSimilarityImpl = getUserSimilarity();

    while (users.hasNext()) {
      User user = users.next();
      if (sampleForUser() && !userID.equals(user.getID())) {
        double theSimilarity = userSimilarityImpl.userSimilarity(theUser, user);
        if (!Double.isNaN(theSimilarity) && theSimilarity >= threshold) {
          neighborhood.add(user);
        }
      }
    }
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    List<User> users = new ArrayList<User>(3);
    users.add(getUser("test1", 0.1));
    users.add(getUser("test2", 0.2, 0.6));
    users.add(getUser("test3", 0.4, 0.9));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    List<RecommendedItem> recommended = recommender.recommend("test1", 1);
    assertNotNull(recommended);
    assertEquals(0, recommended.size());
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    users.add(getUser("test2", 0.2, 0.3, 0.3, 0.6));
    users.add(getUser("test3", 0.4, 0.4, 0.5, 0.9));
    users.add(getUser("test4", 0.1, 0.4, 0.5, 0.8, 0.9, 1.0));
    users.add(getUser("test5", 0.2, 0.3, 0.6, 0.7, 0.1, 0.2));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    List<RecommendedItem> fewRecommended = recommender.recommend("test1", 2);
    List<RecommendedItem> moreRecommended = recommender.recommend("test1", 4);
    for (int i = 0; i < fewRecommended.size(); i++) {
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    List<User> users = new ArrayList<User>(3);
    users.add(getUser("test1", 0.1, 0.2));
    users.add(getUser("test2", 0.2, 0.3, 0.3, 0.6));
    users.add(getUser("test3", 0.4, 0.4, 0.5, 0.9));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    List<RecommendedItem> originalRecommended = recommender.recommend("test1", 2);
    List<RecommendedItem> rescoredRecommended =
            recommender.recommend("test1", 2, new ReversingRescorer<Item>());
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    users.add(getUser("test1", 0.1, 0.3));
    users.add(getUser("test2", 0.2, 0.3, 0.3));
    users.add(getUser("test3", 0.4, 0.3, 0.5));
    users.add(getUser("test4", 0.7, 0.3, 0.8, 0.9));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    assertEquals(0.9, recommender.estimatePreference("test3", "3"));
  }
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    users.add(getUser("test1", 0.1, 0.3));
    users.add(getUser("test2", 0.2, 0.3, 0.3));
    users.add(getUser("test3", 0.4, 0.3, 0.5));
    users.add(getUser("test4", 0.7, 0.3, 0.8));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    List<RecommendedItem> recommended = recommender.recommend("test1", 1);
    assertNotNull(recommended);
    assertEquals(1, recommended.size());
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    users.add(getUser("test2", 0.2, 0.3, 0.3, 0.6));
    users.add(getUser("test3", 0.4, 0.4, 0.5, 0.9));
    users.add(getUser("test4", 0.1, 0.4, 0.5, 0.8, 0.9, 1.0));
    users.add(getUser("test5", 0.2, 0.3, 0.6, 0.7, 0.1, 0.2));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, dataModel);
    Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
    List<RecommendedItem> fewRecommended = recommender.recommend("test1", 2);
    List<RecommendedItem> moreRecommended = recommender.recommend("test1", 4);
    for (int i = 0; i < fewRecommended.size(); i++) {
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    List<User> users = new ArrayList<User>(3);
    users.add(getUser("test1", 0.1, 0.2));
    users.add(getUser("test2", 0.2, 0.3, 0.3, 0.6));
    users.add(getUser("test3", 0.4, 0.4, 0.5, 0.9));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(1, similarity, dataModel);
    Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
    List<RecommendedItem> originalRecommended = recommender.recommend("test1", 2);
    List<RecommendedItem> rescoredRecommended =
            recommender.recommend("test1", 2, new ReversingRescorer<Item>());
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    users.add(getUser("test1", 0.1, 0.2));
    users.add(getUser("test2", 0.2, 0.3, 0.3, 0.6));
    users.add(getUser("test3", 0.4, 0.4, 0.5, 0.9));
    users.add(getUser("test4"));
    DataModel dataModel = new GenericDataModel(users);
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(3, similarity, dataModel);
    UserBasedRecommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
    Collection<User> mostSimilar = recommender.mostSimilarUsers("test4", 3);
    assertNotNull(mostSimilar);
    assertEquals(0, mostSimilar.size());
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    assertEquals(0, mostSimilar.size());
  }

  private static UserBasedRecommender buildRecommender() throws Exception {
    DataModel dataModel = new GenericDataModel(getMockUsers());
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(1, similarity, dataModel);
    return new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
  }
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  • mia.recommender.ch03.IREvaluatorBooleanPrefIntro2
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  • mia.recommender.ch04.GroupLensDataModelIntro
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