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

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

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    assertEquals(5, recommendCount.intValue());

    // Results from this recommend() method can be cached...
    IDRescorer rescorer = NullRescorer.getItemInstance();
    cachingRecommender.refresh(null);
    cachingRecommender.recommend(1, 1, rescorer);
    assertEquals(6, recommendCount.intValue());
    cachingRecommender.recommend(2, 1, rescorer);
    assertEquals(7, recommendCount.intValue());
    cachingRecommender.recommend(1, 1, rescorer);
    assertEquals(7, recommendCount.intValue());
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    // Results from this recommend() method can be cached...
    IDRescorer rescorer = NullRescorer.getItemInstance();
    cachingRecommender.refresh(null);
    cachingRecommender.recommend(1, 1, rescorer);
    assertEquals(6, recommendCount.intValue());
    cachingRecommender.recommend(2, 1, rescorer);
    assertEquals(7, recommendCount.intValue());
    cachingRecommender.recommend(1, 1, rescorer);
    assertEquals(7, recommendCount.intValue());
    cachingRecommender.recommend(2, 1, rescorer);
    assertEquals(7, recommendCount.intValue());
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            });
    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);
    for (int i = 0; i < fewRecommended.size(); i++) {
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    cachingRecommender.refresh(null);
    cachingRecommender.recommend(1, 1, rescorer);
    assertEquals(6, recommendCount.intValue());
    cachingRecommender.recommend(2, 1, rescorer);
    assertEquals(7, recommendCount.intValue());
    cachingRecommender.recommend(1, 1, rescorer);
    assertEquals(7, recommendCount.intValue());
    cachingRecommender.recommend(2, 1, rescorer);
    assertEquals(7, recommendCount.intValue());

    // until you switch Rescorers
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    assertEquals(6, recommendCount.intValue());
    cachingRecommender.recommend(2, 1, rescorer);
    assertEquals(7, recommendCount.intValue());
    cachingRecommender.recommend(1, 1, rescorer);
    assertEquals(7, recommendCount.intValue());
    cachingRecommender.recommend(2, 1, rescorer);
    assertEquals(7, recommendCount.intValue());

    // until you switch Rescorers
    cachingRecommender.recommend(1, 1, null);
    assertEquals(8, recommendCount.intValue());
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    assertEquals(7, recommendCount.intValue());
    cachingRecommender.recommend(2, 1, rescorer);
    assertEquals(7, recommendCount.intValue());

    // until you switch Rescorers
    cachingRecommender.recommend(1, 1, null);
    assertEquals(8, recommendCount.intValue());
    cachingRecommender.recommend(2, 1, null);
    assertEquals(9, recommendCount.intValue());

    cachingRecommender.refresh(null);
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    assertEquals(7, recommendCount.intValue());

    // until you switch Rescorers
    cachingRecommender.recommend(1, 1, null);
    assertEquals(8, recommendCount.intValue());
    cachingRecommender.recommend(2, 1, null);
    assertEquals(9, recommendCount.intValue());

    cachingRecommender.refresh(null);
    cachingRecommender.estimatePreference(1, 1);
    assertEquals(10, recommendCount.intValue());
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                    {0.4, 0.5, 0.5, 0.9},
            });
    ItemSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    Optimizer optimizer = new ConjugateGradientOptimizer();
    Recommender recommender = new KnnItemBasedRecommender(dataModel, similarity, optimizer, 5);
    List<RecommendedItem> originalRecommended = recommender.recommend(1, 2);
    List<RecommendedItem> rescoredRecommended =
        recommender.recommend(1, 2, new ReversingRescorer<Long>());
    assertNotNull(originalRecommended);
    assertNotNull(rescoredRecommended);
    assertEquals(2, originalRecommended.size());
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    ItemSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    Optimizer optimizer = new ConjugateGradientOptimizer();
    Recommender recommender = new KnnItemBasedRecommender(dataModel, similarity, optimizer, 5);
    List<RecommendedItem> originalRecommended = recommender.recommend(1, 2);
    List<RecommendedItem> rescoredRecommended =
        recommender.recommend(1, 2, new ReversingRescorer<Long>());
    assertNotNull(originalRecommended);
    assertNotNull(rescoredRecommended);
    assertEquals(2, originalRecommended.size());
    assertEquals(2, rescoredRecommended.size());
    assertEquals(originalRecommended.get(0).getItemID(), rescoredRecommended.get(1).getItemID());
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  }

  @Test
  public void testBestRating() throws Exception {
    Recommender recommender = buildRecommender();
    List<RecommendedItem> recommended = recommender.recommend(1, 1);
    assertNotNull(recommended);
    assertEquals(1, recommended.size());
    RecommendedItem firstRecommended = recommended.get(0);
    // item one should be recommended because it has a greater rating/score
    assertEquals(2, firstRecommended.getItemID());
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