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

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

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      // nDCG
      // In computing, assume relevant IDs have relevance 1 and others 0
      double cumulativeGain = 0.0;
      double idealizedGain = 0.0;
      for (int i = 0; i < numRecommendedItems; i++) {
        RecommendedItem item = recommendedItems.get(i);
        double discount = 1.0 / log2(i + 2.0); // Classical formulation says log(i+1), but i is 0-based here
        if (relevantItemIDs.contains(item.getItemID())) {
          cumulativeGain += discount;
        }
        // otherwise we're multiplying discount by relevance 0 so it doesn't do anything

        // Ideally results would be ordered with all relevant ones first, so this theoretical
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  @Override
  public boolean equals(Object o) {
    if (!(o instanceof GenericRecommendedItem)) {
      return false;
    }
    RecommendedItem other = (RecommendedItem) o;
    return itemID == other.getItemID() && value == other.getValue();
  }
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  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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  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());
    assertEquals(0.1f, firstRecommended.getValue(), EPSILON);
  }
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  public void testMostSimilar() throws Exception {
    ItemBasedRecommender recommender = buildRecommender();
    List<RecommendedItem> similar = recommender.mostSimilarItems(0, 2);
    assertNotNull(similar);
    assertEquals(2, similar.size());
    RecommendedItem first = similar.get(0);
    RecommendedItem second = similar.get(1);
    assertEquals(1, first.getItemID());
    assertEquals(1.0f, first.getValue(), EPSILON);
    assertEquals(2, second.getItemID());
    assertEquals(0.5f, second.getValue(), EPSILON);
  }
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  public void testMostSimilarToMultiple() throws Exception {
    ItemBasedRecommender recommender = buildRecommender2();
    List<RecommendedItem> similar = recommender.mostSimilarItems(new long[] {0, 1}, 2);
    assertNotNull(similar);
    assertEquals(2, similar.size());
    RecommendedItem first = similar.get(0);
    RecommendedItem second = similar.get(1);
    assertEquals(2, first.getItemID());
    assertEquals(0.85f, first.getValue(), EPSILON);
    assertEquals(3, second.getItemID());
    assertEquals(-0.3f, second.getValue(), EPSILON);
  }
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  public void testMostSimilarToMultipleExcludeIfNotSimilarToAll() throws Exception {
    ItemBasedRecommender recommender = buildRecommender2();
    List<RecommendedItem> similar = recommender.mostSimilarItems(new long[] {3, 4}, 2);
    assertNotNull(similar);
    assertEquals(1, similar.size());
    RecommendedItem first = similar.get(0);
    assertEquals(0, first.getItemID());
    assertEquals(0.2f, first.getValue(), EPSILON);
  }
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  public void testMostSimilarToMultipleDontExcludeIfNotSimilarToAll() throws Exception {
    ItemBasedRecommender recommender = buildRecommender2();
    List<RecommendedItem> similar = recommender.mostSimilarItems(new long[] {1, 2, 4}, 10, false);
    assertNotNull(similar);
    assertEquals(2, similar.size());
    RecommendedItem first = similar.get(0);
    RecommendedItem second = similar.get(1);
    assertEquals(0, first.getItemID());
    assertEquals(0.933333333f, first.getValue(), EPSILON);
    assertEquals(3, second.getItemID());
    assertEquals(-0.2f, second.getValue(), EPSILON);
  }
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  public void testRecommendedBecause() throws Exception {
    ItemBasedRecommender recommender = buildRecommender2();
    List<RecommendedItem> recommendedBecause = recommender.recommendedBecause(1, 4, 3);
    assertNotNull(recommendedBecause);
    assertEquals(3, recommendedBecause.size());
    RecommendedItem first = recommendedBecause.get(0);
    RecommendedItem second = recommendedBecause.get(1);
    RecommendedItem third = recommendedBecause.get(2);
    assertEquals(2, first.getItemID());
    assertEquals(0.99f, first.getValue(), EPSILON);
    assertEquals(3, second.getItemID());
    assertEquals(0.4f, second.getValue(), EPSILON);
    assertEquals(0, third.getItemID());
    assertEquals(0.2f, third.getValue(), EPSILON);
  }
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  public void testRecommender() throws Exception {
    Recommender recommender = new RandomRecommender(getDataModel());
    List<RecommendedItem> recommended = recommender.recommend(1, 1);
    assertNotNull(recommended);
    assertEquals(1, recommended.size());
    RecommendedItem firstRecommended = recommended.get(0);
    assertEquals(2, firstRecommended.getItemID());
    recommender.refresh(null);
    assertEquals(2, firstRecommended.getItemID());
  }
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Related Classes of org.apache.mahout.cf.taste.recommender.RecommendedItem

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  • org.apache.mahout.cf.taste.hadoop.item.RecommenderJobTest
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  • org.apache.mahout.cf.taste.hadoop.RecommendedItemsWritable
  • org.apache.mahout.cf.taste.impl.eval.GenericRecommenderIRStatsEvaluator
  • org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommenderTest
  • org.apache.mahout.cf.taste.impl.recommender.GenericRecommendedItem
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