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

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

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          } catch (NoSuchUserException nsee) {
            continue; // Oops we excluded all prefs for the user -- just move on
          }
         
          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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/** <p>Tests {@link GenericUserBasedRecommender}.</p> */
public final class GenericUserBasedRecommenderTest extends TasteTestCase {

  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(0.3f, firstRecommended.getValue());
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                    {0.2, 0.3, 0.6, 0.7, 0.1, 0.2},
            });
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, dataModel);
    Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
    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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            });
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, dataModel);
    Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
    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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                    {0.4, 0.4, 0.5, 0.9},
            });
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(1, similarity, dataModel);
    Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
    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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    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(1, similarity, dataModel);
    Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
    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(0.3f, recommender.estimatePreference(1, 2));
  }

  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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  public void testRecommender() throws Exception {
    AtomicInteger recommendCount = new AtomicInteger();
    Recommender mockRecommender = new MockRecommender(recommendCount);

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

    Recommender cachingRecommender = new CachingRecommender(mockRecommender);
    cachingRecommender.recommend(1, 1);
    assertEquals(1, recommendCount.get());
    cachingRecommender.recommend(2, 1);
    assertEquals(2, recommendCount.get());
    cachingRecommender.recommend(1, 1);
    assertEquals(2, recommendCount.get());
    cachingRecommender.recommend(2, 1);
    assertEquals(2, recommendCount.get());
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    Recommender cachingRecommender = new CachingRecommender(mockRecommender);
    cachingRecommender.recommend(1, 1);
    assertEquals(1, recommendCount.get());
    cachingRecommender.recommend(2, 1);
    assertEquals(2, recommendCount.get());
    cachingRecommender.recommend(1, 1);
    assertEquals(2, recommendCount.get());
    cachingRecommender.recommend(2, 1);
    assertEquals(2, recommendCount.get());
    cachingRecommender.refresh(null);
    cachingRecommender.recommend(1, 1);
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