Package org.apache.mahout.cf.taste.recommender

Examples of org.apache.mahout.cf.taste.recommender.Recommender.recommend()

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    UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(3, userSimilarity, model);
    Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, userSimilarity);
    assertEquals(1, recommender.recommend(123, 3).size());
    assertEquals(0, recommender.recommend(234, 3).size());
    assertEquals(1, recommender.recommend(345, 3).size());

    // Make sure this doesn't throw an exception
    model.refresh(null);
  }
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  public void testRecommender() throws Exception {
    MutableInt recommendCount = new MutableInt();
    Recommender mockRecommender = new MockRecommender(recommendCount);

    Recommender cachingRecommender = new CachingRecommender(mockRecommender);
    cachingRecommender.recommend(1, 1);
    assertEquals(1, recommendCount.intValue());
    cachingRecommender.recommend(2, 1);
    assertEquals(2, recommendCount.intValue());
    cachingRecommender.recommend(1, 1);
    assertEquals(2, recommendCount.intValue());
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    Recommender mockRecommender = new MockRecommender(recommendCount);

    Recommender cachingRecommender = new CachingRecommender(mockRecommender);
    cachingRecommender.recommend(1, 1);
    assertEquals(1, recommendCount.intValue());
    cachingRecommender.recommend(2, 1);
    assertEquals(2, recommendCount.intValue());
    cachingRecommender.recommend(1, 1);
    assertEquals(2, recommendCount.intValue());
    cachingRecommender.recommend(2, 1);
    assertEquals(2, recommendCount.intValue());
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    Recommender cachingRecommender = new CachingRecommender(mockRecommender);
    cachingRecommender.recommend(1, 1);
    assertEquals(1, recommendCount.intValue());
    cachingRecommender.recommend(2, 1);
    assertEquals(2, recommendCount.intValue());
    cachingRecommender.recommend(1, 1);
    assertEquals(2, recommendCount.intValue());
    cachingRecommender.recommend(2, 1);
    assertEquals(2, recommendCount.intValue());
    cachingRecommender.refresh(null);
    cachingRecommender.recommend(1, 1);
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    assertEquals(1, recommendCount.intValue());
    cachingRecommender.recommend(2, 1);
    assertEquals(2, recommendCount.intValue());
    cachingRecommender.recommend(1, 1);
    assertEquals(2, recommendCount.intValue());
    cachingRecommender.recommend(2, 1);
    assertEquals(2, recommendCount.intValue());
    cachingRecommender.refresh(null);
    cachingRecommender.recommend(1, 1);
    assertEquals(3, recommendCount.intValue());
    cachingRecommender.recommend(2, 1);
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    cachingRecommender.recommend(1, 1);
    assertEquals(2, recommendCount.intValue());
    cachingRecommender.recommend(2, 1);
    assertEquals(2, recommendCount.intValue());
    cachingRecommender.refresh(null);
    cachingRecommender.recommend(1, 1);
    assertEquals(3, recommendCount.intValue());
    cachingRecommender.recommend(2, 1);
    assertEquals(4, recommendCount.intValue());
    cachingRecommender.recommend(3, 1);
    assertEquals(5, recommendCount.intValue());
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public final class KnnItemBasedRecommenderTest extends TasteTestCase {

  @Test
  public void testRecommender() throws Exception {
    Recommender recommender = buildRecommender();
    List<RecommendedItem> recommended = recommender.recommend(1, 1);
    assertNotNull(recommended);
    assertEquals(1, recommended.size());
    RecommendedItem firstRecommended = recommended.get(0);
    assertEquals(2, firstRecommended.getItemID());
    assertEquals(0.1f, firstRecommended.getValue(), EPSILON);
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    cachingRecommender.recommend(2, 1);
    assertEquals(2, recommendCount.intValue());
    cachingRecommender.refresh(null);
    cachingRecommender.recommend(1, 1);
    assertEquals(3, recommendCount.intValue());
    cachingRecommender.recommend(2, 1);
    assertEquals(4, recommendCount.intValue());
    cachingRecommender.recommend(3, 1);
    assertEquals(5, recommendCount.intValue());

    // Results from this recommend() method can be cached...
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    cachingRecommender.refresh(null);
    cachingRecommender.recommend(1, 1);
    assertEquals(3, recommendCount.intValue());
    cachingRecommender.recommend(2, 1);
    assertEquals(4, recommendCount.intValue());
    cachingRecommender.recommend(3, 1);
    assertEquals(5, recommendCount.intValue());

    // Results from this recommend() method can be cached...
    IDRescorer rescorer = NullRescorer.getItemInstance();
    cachingRecommender.refresh(null);
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                    {0.2, 0.3, 0.6, 0.7, 0.1, 0.2},
            });
    ItemSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    Optimizer optimizer = new ConjugateGradientOptimizer();
    Recommender recommender = new KnnItemBasedRecommender(dataModel, similarity, optimizer, 5);
    List<RecommendedItem> fewRecommended = recommender.recommend(1, 2);
    List<RecommendedItem> moreRecommended = recommender.recommend(1, 4);
    for (int i = 0; i < fewRecommended.size(); i++) {
      assertEquals(fewRecommended.get(i).getItemID(), moreRecommended.get(i).getItemID());
    }
    recommender.refresh(null);
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