public void TestSetPrefMemoryDiffUpdates()
{
DiffStorage diffStorage = new MemoryDiffStorage(model, false, false, long.MaxValue);
Recommender recommender = new SlopeOneRecommender(model, true, true, diffStorage);
Assert.AreEqual(0.5, diffStorage.GetDiff("456", "789").Average, EPSILON);
recommender.SetPreference("A123", "456", 0.7);
Assert.AreEqual(-0.1, diffStorage.GetDiff("456", "789").Average, EPSILON);
}
public void testDiffStdevBehavior()
{
List <User> users = new List <User>(3);
users.Add(GetUser("test1", 0.1, 0.2));
users.Add(GetUser("test2", 0.2, 0.3, 0.6));
DataModel dataModel = new GenericDataModel(users);
Recommender recommender = new SlopeOneRecommender(dataModel);
Assert.AreEqual(0.5, recommender.EstimatePreference("test1", "2"), EPSILON);
}
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);
Recommender recommender = new SlopeOneRecommender(dataModel);
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);
Recommender recommender = new SlopeOneRecommender(dataModel);
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 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);
Recommender recommender = new SlopeOneRecommender(dataModel);
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 TestBestRating()
{
List <User> users = new List <User>(3);
users.Add(GetUser("test1", 0.0, 0.3));
users.Add(GetUser("test2", 0.2, 0.3, 0.3));
users.Add(GetUser("test3", 0.4, 0.3, 0.5));
DataModel dataModel = new GenericDataModel(users);
Recommender recommender = new SlopeOneRecommender(dataModel);
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(0.2400938676203033, firstRecommended.Value, EPSILON);
}
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);
Recommender recommender = new SlopeOneRecommender(dataModel);
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);
}
static void SlopeOneRecommenderTest()
{
Console.WriteLine("SlopeOneRecommenderTest");
var model = new FileDataModel(filePath);
var recommender = new SlopeOneRecommender(model);
var ids = model.getUserIDs();
while (ids.MoveNext())
{
var userId = ids.Current;
var recommendedItems = recommender.recommend(userId, 5);
Console.Write("uid:" + userId);
foreach (var ritem in recommendedItems)
{
Console.Write("(" + ritem.getItemID() + "," + ritem.getValue() + ")");
}
Console.WriteLine();
}
}
public void TestSetPrefMemoryDiffUpdates()
{
DiffStorage diffStorage = new MemoryDiffStorage(model, false, false, long.MaxValue);
Recommender recommender = new SlopeOneRecommender(model, true, true, diffStorage);
Assert.AreEqual(0.5, diffStorage.GetDiff("456", "789").Average, EPSILON);
recommender.SetPreference("A123", "456", 0.7);
Assert.AreEqual(-0.1, diffStorage.GetDiff("456", "789").Average, EPSILON);
}
public void TestBestRating()
{
List<User> users = new List<User>(3);
users.Add(GetUser("test1", 0.0, 0.3));
users.Add(GetUser("test2", 0.2, 0.3, 0.3));
users.Add(GetUser("test3", 0.4, 0.3, 0.5));
DataModel dataModel = new GenericDataModel(users);
Recommender recommender = new SlopeOneRecommender(dataModel);
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(0.2400938676203033, firstRecommended.Value, EPSILON);
}
public void testDiffStdevBehavior()
{
List<User> users = new List<User>(3);
users.Add(GetUser("test1", 0.1, 0.2));
users.Add(GetUser("test2", 0.2, 0.3, 0.6));
DataModel dataModel = new GenericDataModel(users);
Recommender recommender = new SlopeOneRecommender(dataModel);
Assert.AreEqual(0.5, recommender.EstimatePreference("test1", "2"), EPSILON);
}
