Examples of recommend()

Examples of net.myrrix.common.MyrrixRecommender.recommend()

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    RescorerProvider rescorerProvider = getRescorerProvider();
    try {
      IDRescorer rescorer = rescorerProvider == null ? null :
          rescorerProvider.getRecommendRescorer(new long[] {userID}, recommender, getRescorerParams(request));
      Iterable<RecommendedItem> recommended =
          recommender.recommend(userID, getHowMany(request), getConsiderKnownItems(request), rescorer);
      output(request, response, recommended);
    } catch (NoSuchUserException nsue) {
      response.sendError(HttpServletResponse.SC_NOT_FOUND, nsue.toString());
    } catch (NotReadyException nre) {
      response.sendError(HttpServletResponse.SC_SERVICE_UNAVAILABLE, nre.toString());
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Examples of net.myrrix.online.ServerRecommender.recommend()

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      public void process(Long userID, long count) throws ExecutionException {
        IDRescorer rescorer =
            rescorerProvider == null ? null : rescorerProvider.getRecommendRescorer(new long[]{userID}, recommender);
        Iterable<RecommendedItem> recs;
        try {
          recs = recommender.recommend(userID, howMany, rescorer);
        } catch (TasteException te) {
          throw new ExecutionException(te);
        }
        String outLine = AllItemSimilarities.formatOutLine(userID, recs);
        synchronized (out) {
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Examples of org.apache.mahout.cf.taste.impl.recommender.CachingRecommender.recommend()

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    Recommender cachingRecommender = new CachingRecommender(recommender);

    for(int userId: userIds) {
      System.out.println("UserID " + userId);
      List<RecommendedItem> recommendations =
          cachingRecommender.recommend(userId, 2);
      for(RecommendedItem item: recommendations) {
        System.out.println("  item " + item.getItemID() + " score " + item.getValue());
      }
    }
  }
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Examples of org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender.recommend()

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  public void testFile() throws Exception {
    UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, userSimilarity, model);
    Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, userSimilarity);
    assertEquals(2, recommender.recommend(123, 3).size());
    assertEquals(2, 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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Examples of org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender.recommend()

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  public void testFile() throws Exception {
    UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, userSimilarity, model);
    Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, userSimilarity);
    assertEquals(2, recommender.recommend(123, 3).size());
    assertEquals(2, 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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Examples of org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender.recommend()

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    UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, userSimilarity, model);
    Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, userSimilarity);
    assertEquals(2, recommender.recommend(123, 3).size());
    assertEquals(2, 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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Examples of org.apache.mahout.cf.taste.impl.recommender.svd.SVDRecommender.recommend()

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        // Corn:
        // (2*(-0.24019223070763077)+2*(0.8196561646738477)) / (-0.24019223070763077+0.8196561646738477) = 2
        System.out.println("UserBased: Wolf should eat: "+id2thing.toStringID(r.getItemID())+" Rating: "+r.getValue());
      }
      SVDRecommender svdrecommender = new SVDRecommender(model, new SVDPlusPlusFactorizer(model, 4, 1000));
      for(RecommendedItem r : svdrecommender.recommend(id2thing.toLongID("Sheep"), 3)) {
        System.out.println("SVD: Sheep should eat: "+id2thing.toStringID(r.getItemID())+" Rating: "+r.getValue());
      }
    } catch (TasteException e) {
      e.printStackTrace();
    }
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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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Examples of org.apache.mahout.cf.taste.recommender.Recommender.recommend()

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

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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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