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);
IList<GenericItemCorrelation.ItemItemCorrelation> correlations =
new List<GenericItemCorrelation.ItemItemCorrelation>(6);
for (int i = 0; i < 6; i++)
{
for (int j = i + 1; j < 6; j++)
{
correlations.Add(
new GenericItemCorrelation.ItemItemCorrelation(new GenericItem<String>(i.ToString()),
new GenericItem<String>(j.ToString()),
1.0 / (1.0 + (double)i + (double)j)));
}
}
ItemCorrelation correlation = new GenericItemCorrelation(correlations);
Recommender recommender = new GenericItemBasedRecommender(dataModel, 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);
Item item1 = new GenericItem <String>("0");
Item item2 = new GenericItem <String>("1");
Item item3 = new GenericItem <String>("2");
Item item4 = new GenericItem <String>("3");
ICollection <GenericItemCorrelation.ItemItemCorrelation> correlations =
new List <GenericItemCorrelation.ItemItemCorrelation>(6);
correlations.Add(new GenericItemCorrelation.ItemItemCorrelation(item1, item2, 1.0));
correlations.Add(new GenericItemCorrelation.ItemItemCorrelation(item1, item3, 0.5));
correlations.Add(new GenericItemCorrelation.ItemItemCorrelation(item1, item4, 0.2));
correlations.Add(new GenericItemCorrelation.ItemItemCorrelation(item2, item3, 0.7));
correlations.Add(new GenericItemCorrelation.ItemItemCorrelation(item2, item4, 0.5));
correlations.Add(new GenericItemCorrelation.ItemItemCorrelation(item3, item4, 0.9));
ItemCorrelation correlation = new GenericItemCorrelation(correlations);
Recommender recommender = new GenericItemBasedRecommender(dataModel, 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 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);
IList <GenericItemCorrelation.ItemItemCorrelation> correlations =
new List <GenericItemCorrelation.ItemItemCorrelation>(6);
for (int i = 0; i < 6; i++)
{
for (int j = i + 1; j < 6; j++)
{
correlations.Add(
new GenericItemCorrelation.ItemItemCorrelation(new GenericItem <String>(i.ToString()),
new GenericItem <String>(j.ToString()),
1.0 / (1.0 + (double)i + (double)j)));
}
}
ItemCorrelation correlation = new GenericItemCorrelation(correlations);
Recommender recommender = new GenericItemBasedRecommender(dataModel, 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 preferencesFetchedOnlyOnce()
{
var dataModelMock = new DynamicMock( typeof( IDataModel) );
var itemSimilarityMock = new DynamicMock( typeof(IItemSimilarity) );
var candidateItemsStrategyMock = new DynamicMock( typeof (ICandidateItemsStrategy) );
var mostSimilarItemsCandidateItemsStrategyMock =
new DynamicMock( typeof(IMostSimilarItemsCandidateItemsStrategy) );
IPreferenceArray preferencesFromUser = new GenericUserPreferenceArray(
new List<IPreference>() {new GenericPreference(1L, 1L, 5.0f), new GenericPreference(1L, 2L, 4.0f)});
dataModelMock.ExpectAndReturn("GetMinPreference", float.NaN);
dataModelMock.ExpectAndReturn("GetMaxPreference", float.NaN);
dataModelMock.ExpectAndReturn("GetPreferencesFromUser", preferencesFromUser, 1L);
var dataModel = (IDataModel)dataModelMock.MockInstance;
candidateItemsStrategyMock.ExpectAndReturn("GetCandidateItems", new FastIDSet(new long[] { 3L, 4L }),
1L, preferencesFromUser, dataModel);
itemSimilarityMock.ExpectAndReturn("ItemSimilarities", new double[] { 0.5, 0.3 },
3L, preferencesFromUser.GetIDs());
itemSimilarityMock.ExpectAndReturn("ItemSimilarities", new double[] { 0.4, 0.1 },
4L, preferencesFromUser.GetIDs());
//EasyMock.replay(dataModel, itemSimilarity, candidateItemsStrategy, mostSimilarItemsCandidateItemsStrategy);
IRecommender recommender = new GenericItemBasedRecommender((IDataModel)dataModel,
(IItemSimilarity)itemSimilarityMock.MockInstance,
(ICandidateItemsStrategy)candidateItemsStrategyMock.MockInstance,
(IMostSimilarItemsCandidateItemsStrategy)mostSimilarItemsCandidateItemsStrategyMock.MockInstance);
recommender.Recommend(1L, 3);
dataModelMock.Verify();
itemSimilarityMock.Verify();
candidateItemsStrategyMock.Verify();
mostSimilarItemsCandidateItemsStrategyMock.Verify();
//EasyMock.verify(dataModel, itemSimilarity, candidateItemsStrategy, mostSimilarItemsCandidateItemsStrategy);
}
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);
Item item1 = new GenericItem<String>("0");
Item item2 = new GenericItem<String>("1");
Item item3 = new GenericItem<String>("2");
Item item4 = new GenericItem<String>("3");
ICollection<GenericItemCorrelation.ItemItemCorrelation> correlations =
new List<GenericItemCorrelation.ItemItemCorrelation>(6);
correlations.Add(new GenericItemCorrelation.ItemItemCorrelation(item1, item2, 1.0));
correlations.Add(new GenericItemCorrelation.ItemItemCorrelation(item1, item3, 0.5));
correlations.Add(new GenericItemCorrelation.ItemItemCorrelation(item1, item4, 0.2));
correlations.Add(new GenericItemCorrelation.ItemItemCorrelation(item2, item3, 0.7));
correlations.Add(new GenericItemCorrelation.ItemItemCorrelation(item2, item4, 0.5));
correlations.Add(new GenericItemCorrelation.ItemItemCorrelation(item3, item4, 0.9));
ItemCorrelation correlation = new GenericItemCorrelation(correlations);
Recommender recommender = new GenericItemBasedRecommender(dataModel, 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 testRescorer()
{
IDataModel dataModel = getDataModel(
new long[] {1, 2, 3},
new Double?[][] {
new double?[]{0.1, 0.2},
new double?[]{0.2, 0.3, 0.3, 0.6},
new double?[]{0.4, 0.4, 0.5, 0.9},
});
var similarities = new List<GenericItemSimilarity.ItemItemSimilarity>();
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(0, 1, 1.0));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(0, 2, 0.5));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(0, 3, 0.2));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(1, 2, 0.7));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(1, 3, 0.5));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(2, 3, 0.9));
IItemSimilarity similarity = new GenericItemSimilarity(similarities);
IRecommender recommender = new GenericItemBasedRecommender(dataModel, similarity);
IList<IRecommendedItem> originalRecommended = recommender.Recommend(1, 2);
IList<IRecommendedItem> rescoredRecommended =
recommender.Recommend(1, 2, new ReversingRescorer<long>());
Assert.NotNull(originalRecommended);
Assert.NotNull(rescoredRecommended);
Assert.AreEqual(2, originalRecommended.Count);
Assert.AreEqual(2, rescoredRecommended.Count);
Assert.AreEqual(originalRecommended[0].GetItemID(), rescoredRecommended[1].GetItemID());
Assert.AreEqual(originalRecommended[1].GetItemID(), rescoredRecommended[0].GetItemID());
}
public void testHowMany()
{
IDataModel dataModel = getDataModel(
new long[] {1, 2, 3, 4, 5},
new Double?[][] {
new double?[]{0.1, 0.2},
new double?[]{0.2, 0.3, 0.3, 0.6},
new double?[]{0.4, 0.4, 0.5, 0.9},
new double?[]{0.1, 0.4, 0.5, 0.8, 0.9, 1.0},
new double?[]{0.2, 0.3, 0.6, 0.7, 0.1, 0.2},
});
var similarities = new List<GenericItemSimilarity.ItemItemSimilarity>();
for (int i = 0; i < 6; i++) {
for (int j = i + 1; j < 6; j++) {
similarities.Add(
new GenericItemSimilarity.ItemItemSimilarity(i, j, 1.0 / (1.0 + i + j)));
}
}
IItemSimilarity similarity = new GenericItemSimilarity(similarities);
IRecommender recommender = new GenericItemBasedRecommender(dataModel, similarity);
IList<IRecommendedItem> fewRecommended = recommender.Recommend(1, 2);
IList<IRecommendedItem> moreRecommended = recommender.Recommend(1, 4);
for (int i = 0; i < fewRecommended.Count; i++) {
Assert.AreEqual(fewRecommended[i].GetItemID(), moreRecommended[i].GetItemID());
}
recommender.Refresh(null);
for (int i = 0; i < fewRecommended.Count; i++) {
Assert.AreEqual(fewRecommended[i].GetItemID(), moreRecommended[i].GetItemID());
}
}