private static IItemBasedRecommender buildRecommender2()
{
IDataModel dataModel = getDataModel(
new long[] { 1, 2, 3, 4 },
new Double?[][] {
new double?[] { 0.1, 0.3, 0.9, 0.8 },
new double?[] { 0.2, 0.3, 0.3, 0.4 },
new double?[] { 0.4, 0.3, 0.5, 0.1, 0.1 },
new double?[] { 0.7, 0.3, 0.8, 0.5, 0.6 },
});
var similarities = new List <GenericItemSimilarity.ItemItemSimilarity>();
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(0, 1, 1.0));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(0, 2, 0.8));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(0, 3, -0.6));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(0, 4, 1.0));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(1, 2, 0.9));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(1, 3, 0.0));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(1, 1, 1.0));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(2, 3, -0.1));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(2, 4, 0.1));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(3, 4, -0.5));
IItemSimilarity similarity = new GenericItemSimilarity(similarities);
return(new GenericItemBasedRecommender(dataModel, similarity));
}
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());
}
private static IItemBasedRecommender buildRecommender()
{
IDataModel dataModel = getDataModel();
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(1, 2, 0.0));
IItemSimilarity similarity = new GenericItemSimilarity(similarities);
return(new GenericItemBasedRecommender(dataModel, similarity));
}
public void testAllSimilaritiesWithoutIndex()
{
List<GenericItemSimilarity.ItemItemSimilarity> itemItemSimilarities =
new List<GenericItemSimilarity.ItemItemSimilarity>() {new GenericItemSimilarity.ItemItemSimilarity(1L, 2L, 0.2),
new GenericItemSimilarity.ItemItemSimilarity(1L, 3L, 0.2),
new GenericItemSimilarity.ItemItemSimilarity(2L, 1L, 0.2),
new GenericItemSimilarity.ItemItemSimilarity(3L, 5L, 0.2),
new GenericItemSimilarity.ItemItemSimilarity(3L, 4L, 0.2)};
IItemSimilarity similarity = new GenericItemSimilarity(itemItemSimilarities);
Assert.True(containsExactly(similarity.AllSimilarItemIDs(1L), 2L, 3L));
Assert.True(containsExactly(similarity.AllSimilarItemIDs(2L), 1L));
Assert.True(containsExactly(similarity.AllSimilarItemIDs(3L), 1L, 5L, 4L));
Assert.True(containsExactly(similarity.AllSimilarItemIDs(4L), 3L));
Assert.True(containsExactly(similarity.AllSimilarItemIDs(5L), 3L));
}
public void testFileItemItemSimilarityIterable()
{
Iterable <ItemItemSimilarity> similarityIterable = new FileItemItemSimilarityIterable(testFile);
GenericItemSimilarity similarity = new GenericItemSimilarity(similarityIterable);
Assert.AreEqual(0.125, similarity.itemSimilarity(1L, 5L), EPSILON);
Assert.AreEqual(0.125, similarity.itemSimilarity(5L, 1L), EPSILON);
Assert.AreEqual(0.5, similarity.itemSimilarity(1L, 7L), EPSILON);
Assert.AreEqual(0.5, similarity.itemSimilarity(7L, 1L), EPSILON);
Assert.True(Double.IsNaN(similarity.itemSimilarity(7L, 8L)));
double[] valuesForOne = similarity.itemSimilarities(1L, new long[] { 5L, 7L });
Assert.NotNull(valuesForOne);
Assert.AreEqual(2, valuesForOne.Length);
Assert.AreEqual(0.125, valuesForOne[0], EPSILON);
Assert.AreEqual(0.5, valuesForOne[1], EPSILON);
}
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());
}
}
public void testSimple()
{
List<GenericItemSimilarity.ItemItemSimilarity> similarities = new List<GenericItemSimilarity.ItemItemSimilarity>();
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(1, 2, 0.5));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(2, 1, 0.6));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(1, 1, 0.5));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(1, 3, 0.3));
GenericItemSimilarity itemCorrelation = new GenericItemSimilarity(similarities);
Assert.AreEqual(1.0, itemCorrelation.ItemSimilarity(1, 1), EPSILON);
Assert.AreEqual(0.6, itemCorrelation.ItemSimilarity(1, 2), EPSILON);
Assert.AreEqual(0.6, itemCorrelation.ItemSimilarity(2, 1), EPSILON);
Assert.AreEqual(0.3, itemCorrelation.ItemSimilarity(1, 3), EPSILON);
Assert.True(Double.IsNaN(itemCorrelation.ItemSimilarity(3, 4)));
}
public void testFromCorrelation()
{
IDataModel dataModel = getDataModel(
new long[] {1, 2, 3},
new Double?[][] {
new double?[] {1.0, 2.0},
new double?[] {2.0, 5.0},
new double?[] {3.0, 6.0},
});
IItemSimilarity otherSimilarity = new PearsonCorrelationSimilarity(dataModel);
IItemSimilarity itemSimilarity = new GenericItemSimilarity(otherSimilarity, dataModel);
assertCorrelationEquals(1.0, itemSimilarity.ItemSimilarity(0, 0));
assertCorrelationEquals(0.960768922830523, itemSimilarity.ItemSimilarity(0, 1));
}
private static IItemBasedRecommender buildRecommender2()
{
IDataModel dataModel = getDataModel(
new long[] {1, 2, 3, 4},
new Double?[][] {
new double?[]{0.1, 0.3, 0.9, 0.8},
new double?[]{0.2, 0.3, 0.3, 0.4},
new double?[]{0.4, 0.3, 0.5, 0.1, 0.1},
new double?[]{0.7, 0.3, 0.8, 0.5, 0.6},
});
var similarities = new List<GenericItemSimilarity.ItemItemSimilarity>();
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(0, 1, 1.0));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(0, 2, 0.8));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(0, 3, -0.6));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(0, 4, 1.0));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(1, 2, 0.9));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(1, 3, 0.0));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(1, 1, 1.0));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(2, 3, -0.1));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(2, 4, 0.1));
similarities.Add(new GenericItemSimilarity.ItemItemSimilarity(3, 4, -0.5));
IItemSimilarity similarity = new GenericItemSimilarity(similarities);
return new GenericItemBasedRecommender(dataModel, similarity);
}
private static IItemBasedRecommender buildRecommender()
{
IDataModel dataModel = getDataModel();
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(1, 2, 0.0));
IItemSimilarity similarity = new GenericItemSimilarity(similarities);
return new GenericItemBasedRecommender(dataModel, similarity);
}
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());
}
}
public void testFileItemItemSimilarityIterable() {
Iterable<ItemItemSimilarity> similarityIterable = new FileItemItemSimilarityIterable(testFile);
GenericItemSimilarity similarity = new GenericItemSimilarity(similarityIterable);
Assert.AreEqual(0.125, similarity.itemSimilarity(1L, 5L), EPSILON);
Assert.AreEqual(0.125, similarity.itemSimilarity(5L, 1L), EPSILON);
Assert.AreEqual(0.5, similarity.itemSimilarity(1L, 7L), EPSILON);
Assert.AreEqual(0.5, similarity.itemSimilarity(7L, 1L), EPSILON);
Assert.True(Double.IsNaN(similarity.itemSimilarity(7L, 8L)));
double[] valuesForOne = similarity.itemSimilarities(1L, new long[] { 5L, 7L });
Assert.NotNull(valuesForOne);
Assert.AreEqual(2, valuesForOne.Length);
Assert.AreEqual(0.125, valuesForOne[0], EPSILON);
Assert.AreEqual(0.5, valuesForOne[1], EPSILON);
}
