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 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 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());
}
}
internal RecommendedBecauseEstimator(GenericItemBasedRecommender r, long userID, long recommendedItemID)
{
this.r = r;
this.userID = userID;
this.recommendedItemID = recommendedItemID;
}
internal Estimator(GenericItemBasedRecommender recommender, long userID, IPreferenceArray preferencesFromUser)
{
this.recommender = recommender;
this.userID = userID;
this.preferencesFromUser = preferencesFromUser;
}