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

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

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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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          List<User> trainingUsers = new ArrayList<User>(dataModel.getNumUsers());
          for (User user2 : dataModel.getUsers()) {
            processOtherUser(id, relevantItems, trainingUsers, user2);
          }
          DataModel trainingModel = new GenericDataModel(trainingUsers);
          Recommender recommender = recommenderBuilder.buildRecommender(trainingModel);

          try {
            trainingModel.getUser(id);
          } catch (NoSuchUserException nsee) {
            continue; // Oops we excluded all prefs for the user -- just move on
          }

          int intersectionSize = 0;
          List<RecommendedItem> recommendedItems = recommender.recommend(id, at, rescorer);
          for (RecommendedItem recommendedItem : recommendedItems) {
            if (relevantItems.contains(recommendedItem.getItem())) {
              intersectionSize++;
            }
          }
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      }
    }

    DataModel trainingModel = new GenericDataModel(trainingUsers);

    Recommender recommender = recommenderBuilder.buildRecommender(trainingModel);

    double result = getEvaluation(testUserPrefs, recommender);
    log.info("Evaluation result: " + result);
    return result;
  }
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    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());
    recommender.refresh(null);
    assertNotNull(recommended);
    assertEquals(0, recommended.size());
  }
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    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++) {
      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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    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>());
    assertNotNull(originalRecommended);
    assertNotNull(rescoredRecommended);
    assertEquals(2, originalRecommended.size());
    assertEquals(2, rescoredRecommended.size());
    assertEquals(originalRecommended.get(0).getItem(), rescoredRecommended.get(1).getItem());
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    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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