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

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

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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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      // 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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    for (Entry<Long,List<RecommendedItem>> entry : recommendations.entrySet()) {
      long userID = entry.getKey();
      List<RecommendedItem> items = entry.getValue();
      assertNotNull(items);
      RecommendedItem item1 = items.get(0);

      if (userID == 1L) {
        assertEquals(1, items.size());
        assertEquals(4L, item1.getItemID());
        assertEquals(4.3, item1.getValue(), 0.05);
      }
      if (userID == 2L) {
        assertEquals(1, items.size());
        assertEquals(2L, item1.getItemID());
        assertEquals(3.3, item1.getValue(), 0.05);
      }
      if (userID == 3L) {
        assertEquals(2, items.size());
        assertEquals(3L, item1.getItemID());
        assertEquals(4.1, item1.getValue(), 0.05);
        RecommendedItem item2 = items.get(1);
        assertEquals(1L, item2.getItemID());
        assertEquals(3.7, item2.getValue(), 0.05);
      }
      if (userID == 4L) {
        assertEquals(2, items.size());
        assertEquals(2L, item1.getItemID());
        assertEquals(4.0, item1.getValue(), 0.05);
        RecommendedItem item2 = items.get(1);
        assertEquals(3L, item2.getItemID());
        assertEquals(3.5, item2.getValue(), 0.05);
      }
    }

    Map<Pair<Long, Long>, Double> similarities = readSimilarities(new File(similaritiesOutputDir, "part-r-00000"));
    assertEquals(6, similarities.size());
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    Map<Long,List<RecommendedItem>> recommendations = readRecommendations(new File(outputDir, "part-r-00000"));

    List<RecommendedItem> recommendedToCow = recommendations.get(3L);
    assertEquals(2, recommendedToCow.size());

    RecommendedItem item1 = recommendedToCow.get(0);
    RecommendedItem item2 = recommendedToCow.get(1);

    assertEquals(1L, item1.getItemID());
    assertEquals(3L, item2.getItemID());

    /* predicted pref must be the sum of similarities:
    *    item1: coocc(burger, hotdog) + coocc(burger, icecream) = 3
    *    item2: coocc(berries, hotdog) + coocc(berries, icecream) = 2 */
    assertEquals(3, item1.getValue(), 0.05);
    assertEquals(2, item2.getValue(), 0.05);
  }
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     assertEquals(1, recommendations.size());
     assertTrue(recommendations.containsKey(4L));
     assertEquals(1, recommendations.get(4L).size());

     /* berries should have been recommended to the donkey */
     RecommendedItem recommendedItem = recommendations.get(4L).get(0);
     assertEquals(3L, recommendedItem.getItemID());
     assertEquals(3.5, recommendedItem.getValue(), 0.05);
   }
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  • org.apache.mahout.cf.taste.hadoop.item.RecommenderJobTest
  • org.apache.mahout.cf.taste.hadoop.MutableRecommendedItem
  • 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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