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 },
});
IUserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
IUserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, dataModel);
IRecommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, 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());
}
}
public void testIsolatedUser()
{
IDataModel dataModel = getDataModel(
new long[] { 1, 2, 3, 4 },
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?[] { null, null, null, null, 1.0 },
});
IUserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
IUserNeighborhood neighborhood = new NearestNUserNeighborhood(3, similarity, dataModel);
IUserBasedRecommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
long[] mostSimilar = recommender.MostSimilarUserIDs(4, 3);
Assert.NotNull(mostSimilar);
Assert.AreEqual(0, mostSimilar.Length);
}
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, 0.5, 0.5, 0.9 },
});
IUserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
IUserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, dataModel);
IRecommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, 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());
}
internal Estimator(GenericUserBasedRecommender r, long theUserID, long[] theNeighborhood)
{
this.r = r;
this.theUserID = theUserID;
this.theNeighborhood = theNeighborhood;
}
