public void TestEstimatePref()
{
List <User> users = new List <User>(4);
users.Add(GetUser("test1", 0.1, 0.3));
users.Add(GetUser("test2", 0.2, 0.3, 0.3));
users.Add(GetUser("test3", 0.4, 0.3, 0.5));
users.Add(GetUser("test4", 0.7, 0.3, 0.8, 0.9));
DataModel dataModel = new GenericDataModel(users);
UserCorrelation correlation = new PearsonCorrelation(dataModel);
ClusterSimilarity similarity = new FarthestNeighborClusterSimilarity(correlation);
Recommender recommender = new TreeClusteringRecommender(dataModel, similarity, 2);
Assert.AreEqual(0.9, recommender.EstimatePreference("test3", "3"));
}
public void TestNoRecommendations()
{
List <User> users = new List <User>(3);
users.Add(GetUser("test1", 0.1));
users.Add(GetUser("test2", 0.2, 0.6));
users.Add(GetUser("test3", 0.4, 0.9));
DataModel dataModel = new GenericDataModel(users);
UserCorrelation correlation = new PearsonCorrelation(dataModel);
ClusterSimilarity similarity = new FarthestNeighborClusterSimilarity(correlation);
Recommender recommender = new TreeClusteringRecommender(dataModel, similarity, 2);
IList <RecommendedItem> recommended = recommender.Recommend("test1", 1);
Assert.IsNotNull(recommended);
Assert.AreEqual(0, recommended.Count);
}
public void testHowMany()
{
List <User> users = new List <User>(3);
users.Add(GetUser("test1", 0.1, 0.2));
users.Add(GetUser("test2", 0.2, 0.3, 0.3, 0.6));
users.Add(GetUser("test3", 0.4, 0.4, 0.5, 0.9));
users.Add(GetUser("test4", 0.1, 0.4, 0.5, 0.8, 0.9, 1.0));
users.Add(GetUser("test5", 0.2, 0.3, 0.6, 0.7, 0.1, 0.2));
DataModel dataModel = new GenericDataModel(users);
UserCorrelation correlation = new PearsonCorrelation(dataModel);
ClusterSimilarity similarity = new FarthestNeighborClusterSimilarity(correlation);
Recommender recommender = new TreeClusteringRecommender(dataModel, similarity, 2);
IList <RecommendedItem> fewRecommended = recommender.Recommend("test1", 2);
IList <RecommendedItem> moreRecommended = recommender.Recommend("test1", 4);
for (int i = 0; i < fewRecommended.Count; i++)
{
Assert.AreEqual(fewRecommended[i].Item, moreRecommended[i].Item);
}
}
public void TestRescorer()
{
List <User> users = new List <User>(3);
users.Add(GetUser("test1", 0.1, 0.2));
users.Add(GetUser("test2", 0.2, 0.3, 0.3, 0.6));
users.Add(GetUser("test3", 0.4, 0.4, 0.5, 0.9));
DataModel dataModel = new GenericDataModel(users);
UserCorrelation correlation = new PearsonCorrelation(dataModel);
ClusterSimilarity similarity = new FarthestNeighborClusterSimilarity(correlation);
Recommender recommender = new TreeClusteringRecommender(dataModel, similarity, 2);
IList <RecommendedItem> originalRecommended = recommender.Recommend("test1", 2);
IList <RecommendedItem> rescoredRecommended =
recommender.Recommend("test1", 2, new ReversingRescorer <Item>());
Assert.IsNotNull(originalRecommended);
Assert.IsNotNull(rescoredRecommended);
Assert.AreEqual(2, originalRecommended.Count);
Assert.AreEqual(2, rescoredRecommended.Count);
Assert.AreEqual(originalRecommended[0].Item, rescoredRecommended[1].Item);
Assert.AreEqual(originalRecommended[1].Item, rescoredRecommended[0].Item);
}
public void TestBestRating()
{
List <User> users = new List <User>(4);
users.Add(GetUser("test1", 0.1, 0.3));
users.Add(GetUser("test2", 0.2, 0.3, 0.3));
users.Add(GetUser("test3", 0.4, 0.3, 0.5));
users.Add(GetUser("test4", 0.7, 0.3, 0.8));
DataModel dataModel = new GenericDataModel(users);
UserCorrelation correlation = new PearsonCorrelation(dataModel);
ClusterSimilarity similarity = new FarthestNeighborClusterSimilarity(correlation);
Recommender recommender = new TreeClusteringRecommender(dataModel, similarity, 2);
IList <RecommendedItem> recommended = recommender.Recommend("test1", 1);
Assert.IsNotNull(recommended);
Assert.AreEqual(1, recommended.Count);
RecommendedItem firstRecommended = recommended[0];
// item one should be recommended because it has a greater rating/score
Assert.AreEqual(new GenericItem <String>("2"), firstRecommended.Item);
Assert.AreEqual(0.3, firstRecommended.Value, EPSILON);
}
