List of recommend() Examples

Examples of recommend()

net.myrrix.common.MyrrixRecommender.recommend()
@param userID user for which recommendations are to be computed @param howMany desired number of recommendations @param considerKnownItems if true, items that the user is already associated to are candidatesfor recommendation. Normally this is {@code false}. @param rescorer rescoring function used to modify association strengths before ranking results @return {@link List} of recommended {@link RecommendedItem}s, ordered from most strongly recommend to least @throws NotReadyException if the recommender has no model available yet @throws TasteException if another error occurs
net.myrrix.online.ServerRecommender.recommend()
Like {@link #recommend(long,int,IDRescorer)} but supplies no rescorer.
org.apache.mahout.cf.taste.impl.recommender.CachingRecommender.recommend()
org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender.recommend()
org.apache.mahout.cf.taste.impl.recommender.svd.SVDRecommender.recommend()
org.apache.mahout.cf.taste.recommender.Recommender.recommend()
@param userID user for which recommendations are to be computed @param howMany desired number of recommendations @return {@link List} of recommended {@link RecommendedItem}s, ordered from most strongly recommend to least @throws TasteException if an error occurs while accessing the {@link DataModel}
org.grouplens.lenskit.ItemRecommender.recommend()
Recommend all possible items for a user using the default exclude set. @param user The user ID. @return The recommended items. @see #recommend(long,int,Set,Set)

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

   * <a href="http://sourceforge.net/tracker/index.php?func=detail&amp;aid=1396128&amp;group_id=138771&amp;atid=741665">
   * 1396128</a>.
   */
  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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Examples of org.apache.mahout.cf.taste.recommender.Recommender.recommend()


public final class ItemAverageRecommenderTest extends TasteTestCase {


  public void testRecommender() throws Exception {
    Recommender recommender = new ItemAverageRecommender(getDataModel());
    List<RecommendedItem> recommended = recommender.recommend(1, 1);
    assertNotNull(recommended);
    assertEquals(1, recommended.size());
    RecommendedItem firstRecommended = recommended.get(0);
    assertEquals(2, firstRecommended.getItemID());
    assertEquals(0.53333336f, firstRecommended.getValue());

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

                    {0.4, 0.9},
            });
    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    List<RecommendedItem> recommended = recommender.recommend(1, 1);
    assertNotNull(recommended);
    assertEquals(0, recommended.size());
    recommender.refresh(null);
    assertNotNull(recommended);
    assertEquals(0, recommended.size());

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

            });


    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    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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Examples of org.apache.mahout.cf.taste.recommender.Recommender.recommend()


    UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
    ClusterSimilarity clusterSimilarity = new FarthestNeighborClusterSimilarity(similarity);
    Recommender recommender = new TreeClusteringRecommender(dataModel, clusterSimilarity, 2);
    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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Examples of org.grouplens.lenskit.ItemRecommender.recommend()

            if (irec == null) {
                return Optional.absent();
            }
            LongSet candidates = key.candidates.select(LenskitTestUser.this);
            LongSet excludes = key.exclude.select(LenskitTestUser.this);
            return Optional.of(irec.recommend(getUserId(), key.listSize, candidates, excludes));
        }
    }
}

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