{0.1, 0.2},
{0.2, 0.3, 0.3, 0.6},
{0.4, 0.5, 0.5, 0.9},
});
UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, dataModel);
Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
List<RecommendedItem> originalRecommended = recommender.recommend(1, 4, null, true);
List<RecommendedItem> rescoredRecommended = recommender.recommend(1, 4, new ReversingRescorer<Long>(), true);
assertNotNull(originalRecommended);
assertNotNull(rescoredRecommended);