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

Examples of org.apache.mahout.cf.taste.recommender.Recommender

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* <p>Tests {@link GenericItemBasedRecommender}.</p>
*/
public final class GenericItemBasedRecommenderTest extends TasteTestCase {

  public void testRecommender() throws Exception {
    Recommender recommender = buildRecommender();
    List<RecommendedItem> recommended = recommender.recommend("test1", 1);
    assertNotNull(recommended);
    assertEquals(1, recommended.size());
    RecommendedItem firstRecommended = recommended.get(0);
    assertEquals(new GenericItem<String>("2"), firstRecommended.getItem());
    assertEquals(0.1, firstRecommended.getValue(), EPSILON);
    recommender.refresh(null);
    assertEquals(new GenericItem<String>("2"), firstRecommended.getItem());
    assertEquals(0.1, firstRecommended.getValue(), EPSILON);
  }
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                                                               new GenericItem<String>(String.valueOf(j)),
                                                               1.0 / (1.0 + (double) i + (double) j)));
      }
    }
    ItemSimilarity similarity = new GenericItemSimilarity(similarities);
    Recommender recommender = new GenericItemBasedRecommender(dataModel, similarity);
    List<RecommendedItem> fewRecommended = recommender.recommend("test1", 2);
    List<RecommendedItem> moreRecommended = recommender.recommend("test1", 4);
    for (int i = 0; i < fewRecommended.size(); i++) {
      assertEquals(fewRecommended.get(i).getItem(), moreRecommended.get(i).getItem());
    }
    recommender.refresh(null);
    for (int i = 0; i < fewRecommended.size(); i++) {
      assertEquals(fewRecommended.get(i).getItem(), moreRecommended.get(i).getItem());
    }
  }
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                    {0.4, 0.4, 0.5, 0.9},
                    {0.1, 0.4, 0.5, 0.8, 0.9, 1.0},
                    {0.2, 0.3, 0.6, 0.7, 0.1, 0.2},
            });

    Recommender recommender = new SlopeOneRecommender(dataModel);
    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++) {
      assertEquals(fewRecommended.get(i).getItemID(), moreRecommended.get(i).getItemID());
    }
  }
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                    {0.1, 0.2},
                    {0.2, 0.3, 0.3, 0.6},
                    {0.4, 0.4, 0.5, 0.9},
            });

    Recommender recommender = new SlopeOneRecommender(dataModel);
    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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    assertEquals(originalRecommended.get(1).getItemID(), rescoredRecommended.get(0).getItemID());
  }

  @Test
  public void testEstimatePref() throws Exception {
    Recommender recommender = buildRecommender();
    assertEquals(0.34803885284992736, recommender.estimatePreference(1, 2), EPSILON);
  }
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                    {0.0, 0.3},
                    {0.2, 0.3, 0.3},
                    {0.4, 0.3, 0.5},
            });

    Recommender recommender = new SlopeOneRecommender(dataModel);
    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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                    {0.1, 0.2},
                    {0.2, 0.3, 0.6},
                    {0.3, 0.3, 0.3},
            });

    Recommender recommender = new SlopeOneRecommender(dataModel);
    assertEquals(0.3257085f, recommender.estimatePreference(1, 2), EPSILON);
  }
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      if (size < 2 * at) {
        // Really not enough prefs to meaningfully evaluate this user
        continue;
      }

      Recommender recommender = recommenderBuilder.buildRecommender(trainingModel);

      int intersectionSize = 0;
      List<RecommendedItem> recommendedItems = recommender.recommend(userID, at, rescorer);
      for (RecommendedItem recommendedItem : recommendedItems) {
        if (relevantItemIDs.contains(recommendedItem.getItemID())) {
          intersectionSize++;
        }
      }
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  @Test
  public void testFile() throws Exception {
    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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/** <p>Tests {@link GenericItemBasedRecommender}.</p> */
public final class GenericItemBasedRecommenderTest 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);
    recommender.refresh(null);
    recommended = recommender.recommend(1, 1);
    firstRecommended = recommended.get(0);   
    assertEquals(2, firstRecommended.getItemID());
    assertEquals(0.1f, firstRecommended.getValue(), EPSILON);
  }
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