public void TestItemLoad()
{
DataModel model = CreateModel();
ItemCorrelation itemCorrelation = new PearsonCorrelation(model);
var recommender = new CachingRecommender(new GenericItemBasedRecommender(model, itemCorrelation));
DoTestLoad(recommender, 60);
}
public void TestFullCorrelation1()
{
User user1 = GetUser("test1", 3.0, -2.0);
User user2 = GetUser("test2", 3.0, -2.0);
DataModel dataModel = GetDataModel(user1, user2);
double correlation = new PearsonCorrelation(dataModel).GetUserCorrelation(user1, user2);
AssertCorrelationEquals(1.0, correlation);
}
public void testNoCorrelation1Weighted()
{
User user1 = GetUser("test1", 3.0, -2.0);
User user2 = GetUser("test2", -3.0, 2.0);
DataModel dataModel = GetDataModel(user1, user2);
double correlation = new PearsonCorrelation(dataModel, true).GetUserCorrelation(user1, user2);
AssertCorrelationEquals(-1.0, correlation);
}
public void TestFullCorrelation2()
{
User user1 = GetUser("test1", 3.0, 3.0);
User user2 = GetUser("test2", 3.0, 3.0);
DataModel dataModel = GetDataModel(user1, user2);
double correlation = new PearsonCorrelation(dataModel).GetUserCorrelation(user1, user2);
// Yeah, undefined in this case
Assert.IsTrue(Double.IsNaN(correlation));
}
public void TestUserLoad()
{
DataModel model = CreateModel();
UserCorrelation userCorrelation = new PearsonCorrelation(model);
userCorrelation.PreferenceInferrer = new AveragingPreferenceInferrer(model);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(10, userCorrelation, model);
var recommender =
new CachingRecommender(new GenericUserBasedRecommender(model, neighborhood, userCorrelation));
DoTestLoad(recommender, 20);
}
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 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);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, correlation, dataModel);
Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, correlation);
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 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);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(1, correlation, dataModel);
Recommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, correlation);
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 TestSimple()
{
User user1 = GetUser("test1", 1.0, 2.0, 3.0);
User user2 = GetUser("test2", 2.0, 5.0, 6.0);
DataModel dataModel = GetDataModel(user1, user2);
double correlation = new PearsonCorrelation(dataModel).GetUserCorrelation(user1, user2);
AssertCorrelationEquals(0.9607689228305227, correlation);
}
public void TestIsolatedUser()
{
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"));
DataModel dataModel = new GenericDataModel(users);
UserCorrelation correlation = new PearsonCorrelation(dataModel);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(3, correlation, dataModel);
UserBasedRecommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, correlation);
ICollection<User> mostSimilar = recommender.MostSimilarUsers("test4", 3);
Assert.IsNotNull(mostSimilar);
Assert.AreEqual(0, mostSimilar.Count);
}
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 TestSimpleItem()
{
User user1 = GetUser("test1", 1.0, 2.0);
User user2 = GetUser("test2", 2.0, 5.0);
User user3 = GetUser("test3", 3.0, 6.0);
DataModel dataModel = GetDataModel(user1, user2, user3);
double correlation =
new PearsonCorrelation(dataModel).GetItemCorrelation(dataModel.GetItem("0"), dataModel.GetItem("1"));
AssertCorrelationEquals(0.9607689228305227, correlation);
}
public void TestSimpleItemWeighted()
{
User user1 = GetUser("test1", 1.0, 2.0);
User user2 = GetUser("test2", 2.0, 5.0);
User user3 = GetUser("test3", 3.0, 6.0);
DataModel dataModel = GetDataModel(user1, user2, user3);
ItemCorrelation itemCorrelation = new PearsonCorrelation(dataModel, true);
double correlation = itemCorrelation.GetItemCorrelation(dataModel.GetItem("0"), dataModel.GetItem("1"));
AssertCorrelationEquals(0.9901922307076306, correlation);
}
public void TestNoItemCorrelation3()
{
User user1 = GetUser("test1", 90.0, 70.0);
User user2 = GetUser("test2", 80.0, 80.0);
User user3 = GetUser("test3", 70.0, 90.0);
DataModel dataModel = GetDataModel(user1, user2, user3);
double correlation =
new PearsonCorrelation(dataModel).GetItemCorrelation(dataModel.GetItem("0"), dataModel.GetItem("1"));
AssertCorrelationEquals(-1.0, correlation);
}
public void TestNoItemCorrelation1()
{
User user1 = GetUser("test1", 3.0, -3.0);
User user2 = GetUser("test2", -2.0, 2.0);
DataModel dataModel = GetDataModel(user1, user2);
double correlation =
new PearsonCorrelation(dataModel).GetItemCorrelation(dataModel.GetItem("0"), dataModel.GetItem("1"));
AssertCorrelationEquals(-1.0, correlation);
}
public void TestSimpleWeighted()
{
User user1 = GetUser("test1", 1.0, 2.0, 3.0);
User user2 = GetUser("test2", 2.0, 5.0, 6.0);
DataModel dataModel = GetDataModel(user1, user2);
double correlation = new PearsonCorrelation(dataModel, true).GetUserCorrelation(user1, user2);
AssertCorrelationEquals(0.9901922307076306, correlation);
}
private static UserBasedRecommender buildRecommender()
{
DataModel dataModel = new GenericDataModel(GetMockUsers());
UserCorrelation correlation = new PearsonCorrelation(dataModel);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(1, correlation, dataModel);
return new GenericUserBasedRecommender(dataModel, neighborhood, correlation);
}
public void testNoCorrelation2()
{
Preference pref1 = new GenericPreference(null, new GenericItem<String>("1"), 1.0);
GenericUser<String> user1 = new GenericUser<String>("test1", ScalarToList<Preference>(pref1));
Preference pref2 = new GenericPreference(null, new GenericItem<String>("2"), 1.0);
GenericUser<String> user2 = new GenericUser<String>("test2", ScalarToList<Preference>(pref2));
DataModel dataModel = GetDataModel(user1, user2);
double correlation = new PearsonCorrelation(dataModel).GetUserCorrelation(user1, user2);
Assert.IsTrue(Double.IsNaN(correlation));
}
public void testNoCorrelation3()
{
User user1 = GetUser("test1", 90.0, 80.0, 70.0);
User user2 = GetUser("test2", 70.0, 80.0, 90.0);
DataModel dataModel = GetDataModel(user1, user2);
double correlation = new PearsonCorrelation(dataModel).GetUserCorrelation(user1, user2);
AssertCorrelationEquals(-1.0, correlation);
}
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);
}
