public static void main(String[] args)
{
DataModel model = new FileDataModel(new File(args[0]));
int howMany = 10;
if (args.Length > 1) {
howMany = Integer.parseInt(args[1]);
}
System.out.println("Run Items");
ItemSimilarity similarity = new EuclideanDistanceSimilarity(model);
Recommender recommender = new GenericItemBasedRecommender(model, similarity); // Use an item-item recommender
for (int i = 0; i < LOOPS; i++) {
LoadStatistics loadStats = LoadEvaluator.runLoad(recommender, howMany);
System.out.println(loadStats);
}
System.out.println("Run Users");
UserSimilarity userSim = new EuclideanDistanceSimilarity(model);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(10, userSim, model);
recommender = new GenericUserBasedRecommender(model, neighborhood, userSim);
for (int i = 0; i < LOOPS; i++) {
LoadStatistics loadStats = LoadEvaluator.runLoad(recommender, howMany);
System.out.println(loadStats);
}
}
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 static void main(String[] args)
{
DataModel model = new FileDataModel(new File(args[0]));
int howMany = 10;
if (args.Length > 1)
{
howMany = Integer.parseInt(args[1]);
}
System.out.println("Run Items");
ItemSimilarity similarity = new EuclideanDistanceSimilarity(model);
Recommender recommender = new GenericItemBasedRecommender(model, similarity); // Use an item-item recommender
for (int i = 0; i < LOOPS; i++)
{
LoadStatistics loadStats = LoadEvaluator.runLoad(recommender, howMany);
System.out.println(loadStats);
}
System.out.println("Run Users");
UserSimilarity userSim = new EuclideanDistanceSimilarity(model);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(10, userSim, model);
recommender = new GenericUserBasedRecommender(model, neighborhood, userSim);
for (int i = 0; i < LOOPS; i++)
{
LoadStatistics loadStats = LoadEvaluator.runLoad(recommender, howMany);
System.out.println(loadStats);
}
}
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);
}
}
static void GenericItemBasedRecommenderTestByTanimotoCoefficientSimilarity()
{
Console.WriteLine("GenericItemBasedRecommenderTestByTanimotoCoefficientSimilarity");
System.Diagnostics.Stopwatch watch = new System.Diagnostics.Stopwatch();
watch.Start();
var model = new FileDataModel(filePath);
ItemSimilarity similarity = new TanimotoCoefficientSimilarity(model);
var recommender = new GenericItemBasedRecommender(model, similarity);
var iter = model.getUserIDs();
while (iter.MoveNext())
{
var userId = iter.Current;
var recommendedItems = recommender.recommend(userId, 5);
Console.Write("uid:" + userId);
foreach (var ritem in recommendedItems)
{
Console.Write("(" + ritem.getItemID() + "," + ritem.getValue() + ")");
}
Console.WriteLine();
}
watch.Stop();
Console.WriteLine(watch.ElapsedMilliseconds);
}
public Result RecommendPearsonCorrelationSimilarity(int userId, int movieId)
{
Result result = new Result();
dataModel = new FileDataModel(PathToDataFile, false, FileDataModel.DEFAULT_MIN_RELOAD_INTERVAL_MS, false, ",", userId, movieId);
var removedPrefs = GenericDataModel.preferenceFromUsersRemoved.Values;
var valueToCompare = removedPrefs.FirstOrDefault(i => i.GetItemID() == movieId).GetValue();
var similarity = new PearsonCorrelationSimilarity(dataModel);
var recommender = new GenericItemBasedRecommender(dataModel, similarity);
var preferences = recommender.EstimatePreference(userId, movieId);
result.PredictedValue = preferences;
result.RealValue = removedPrefs.First().GetValue();
return result;
}
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);
}
static void Main(string[] args)
{
// load movies
var movies = new List <MovieRecord>();
using (TextReader reader = File.OpenText(@"data/movies.csv"))
{
CsvReader csv = new CsvReader(reader);
csv.Configuration.Delimiter = ",";
csv.Configuration.MissingFieldFound = null;
while (csv.Read())
{
movies.Add(csv.GetRecord <MovieRecord>());
}
}
// load users
List <int> users = new List <int>();
using (TextReader reader = File.OpenText(@"data/ratings.csv"))
{
CsvReader csv = new CsvReader(reader);
csv.Configuration.Delimiter = ",";
csv.Configuration.MissingFieldFound = null;
while (csv.Read())
{
if (int.TryParse(csv.GetField(0), out int id))
{
if (!users.Contains(id))
{
users.Add(id);
}
}
}
}
// load data model
var model = new FileDataModel("data/ratings.csv", false, FileDataModel.DEFAULT_MIN_RELOAD_INTERVAL_MS, false);
//init values
string similarityType = "Cosine Similarity";
string recommenderType = "Userbased";
int neighborhoodSize = 125;
int resultCount = 10;
List <int> movieIdPredList = new List <int>();
List <int> userIdPredList = new List <int>();
//default recommender
AbstractSimilarity similarity = new UncenteredCosineSimilarity(model);
var neighborhood = new NearestNUserNeighborhood(neighborhoodSize, similarity, model);
AbstractRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
string input = "";
while (input != "X")
{
Console.WriteLine();
Console.WriteLine("Was möchten Sie tun?");
Console.WriteLine("I - Umschalten auf Item-based Ansatz");
Console.WriteLine("U - Umschalten auf User-based Ansatz");
Console.WriteLine("DE - Umschalten auf Cosine Similarity");
Console.WriteLine("PE - Umschalten auf Pearson Correlation");
Console.WriteLine("N - Anzahl der Nachbarn festlegen");
Console.WriteLine("C - Konfiguration neu erstellen und Datendatei neu lesen");
Console.WriteLine("O - Aktuelle Konfiguration ausgeben");
Console.WriteLine("E - Empfehlung durchführen");
Console.WriteLine("P - Prediction für spezifischen Film durchführen");
Console.WriteLine("UADD - Benutzer zur Liste für Vorschläge hinzufügen");
Console.WriteLine("MADD - Film zur Filmliste hinzufügen");
Console.WriteLine("UCLEAR - Benutzerliste leeren");
Console.WriteLine("MCLEAR - Filmliste leeren");
Console.WriteLine("X - Programm beenden");
input = Console.ReadLine();
if (input == "I")
{
recommenderType = "Itembased";
recommender = new GenericItemBasedRecommender(model, similarity);
Console.WriteLine("Recommendertyp auf \"itembased\" geändert.");
}
else if (input == "U")
{
recommenderType = "Userbased";
recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
Console.WriteLine("Recommendertyp auf \"userbased\" geändert.");
}
else if (input == "DE")
{
similarityType = "Cosine Similarity";
similarity = new UncenteredCosineSimilarity(model);
Console.WriteLine("Similaritytyp auf \"Cosine Similarity\" geändert.");
}
else if (input == "PE")
{
similarityType = "Pearson Correlation Similarity";
similarity = new PearsonCorrelationSimilarity(model);
Console.WriteLine("Similaritytyp auf \"Pearson Correlation Similarity\" geändert.");
}
else if (input == "N")
{
Console.WriteLine("Wie groß soll die Nachbarschaft sein?");
string neighborhoodSizeInput = Console.ReadLine();
if (int.TryParse(neighborhoodSizeInput, out int neighborhoodSizeParsed))
{
neighborhoodSize = neighborhoodSizeParsed;
neighborhood = new NearestNUserNeighborhood(neighborhoodSize, similarity, model);
Console.WriteLine("Nachbarschaftsgröße auf " + neighborhoodSize + " geändert.");
}
else
{
Console.WriteLine("Error: Es wurde keine Zahl eingegeben.");
Console.WriteLine("Die Nachbarschaftsgröße bleibt bei " + neighborhoodSize + ".");
}
}
else if (input == "C")
{
Console.WriteLine("Wie groß soll die Nachbarschaft sein?");
string neighborhoodSizeInput = Console.ReadLine();
if (int.TryParse(neighborhoodSizeInput, out int neighborhoodSizeParsed))
{
neighborhoodSize = neighborhoodSizeParsed;
neighborhood = new NearestNUserNeighborhood(neighborhoodSize, similarity, model);
Console.WriteLine("Nachbarschaftsgröße auf " + neighborhoodSize + " geändert.");
}
else
{
Console.WriteLine("Error: Es wurde keine Zahl eingegeben.");
Console.WriteLine("Die Nachbarschaftsgröße bleibt bei " + neighborhoodSize + ".");
}
Console.WriteLine("Wieviele Filme sollen vorgeschlagen werden?");
string resultCountInput = Console.ReadLine();
if (int.TryParse(resultCountInput, out int resultCountParsed))
{
resultCount = resultCountParsed;
}
else
{
Console.WriteLine("Error: Es wurde keine Zahl eingegeben.");
Console.WriteLine("Die Anzahl der vorgeschlagenen Filme bleibt bei " + resultCount + ".");
}
foreach (int userId in userIdPredList)
{
Console.WriteLine("Vorgeschlagene Filme für User mit UserId " + userId);
IList <IRecommendedItem> recommendedItems = recommender.Recommend(userId, resultCount);
foreach (var recItem in recommendedItems)
{
Console.WriteLine("Item: " + recItem.GetItemID() + " (" + movies.First(x => x.movieId == recItem.GetItemID()).title + ") ===> " + recItem.GetValue());
}
Console.WriteLine();
}
// load movies
movies = new List <MovieRecord>();
using (TextReader reader = File.OpenText(@"data/movies.csv"))
{
CsvReader csv = new CsvReader(reader);
csv.Configuration.Delimiter = ",";
csv.Configuration.MissingFieldFound = null;
while (csv.Read())
{
movies.Add(csv.GetRecord <MovieRecord>());
}
}
// load users
users = new List <int>();
using (TextReader reader = File.OpenText(@"data/ratings.csv"))
{
CsvReader csv = new CsvReader(reader);
csv.Configuration.Delimiter = ",";
csv.Configuration.MissingFieldFound = null;
while (csv.Read())
{
if (int.TryParse(csv.GetField(0), out int id))
{
if (!users.Contains(id))
{
users.Add(id);
}
}
}
}
model = new FileDataModel("data/ratings.csv", false, FileDataModel.DEFAULT_MIN_RELOAD_INTERVAL_MS, false);
if (similarityType == "Cosine Similarity")
{
similarity = new UncenteredCosineSimilarity(model);
}
else
{
similarity = new PearsonCorrelationSimilarity(model);
}
neighborhood = new NearestNUserNeighborhood(neighborhoodSize, similarity, model);
if (recommenderType == "Itembased")
{
recommender = new GenericItemBasedRecommender(model, similarity);
}
else
{
recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
}
}
else if (input == "O")
{
int countPredUser = userIdPredList.Count;
int countPredMovies = movieIdPredList.Count;
Console.WriteLine("-----------------------------------------------");
Console.WriteLine("Recommender Typ: " + recommenderType);
Console.WriteLine("Similarity Typ: " + similarityType);
Console.WriteLine("Nachbarschaftsgröße: " + neighborhoodSize);
Console.WriteLine("Anzahl Ergebnisse: " + resultCount);
Console.WriteLine("Anzahl Benutzer: " + countPredUser);
Console.WriteLine("Anzahl vorgeschlagene Filme: " + countPredMovies);
Console.WriteLine("-----------------------------------------------");
}
else if (input == "E")
{
Console.WriteLine("Wieviele Filme sollen vorgeschlagen werden?");
string resultCountInput = Console.ReadLine();
if (int.TryParse(resultCountInput, out int resultCountParsed))
{
resultCount = resultCountParsed;
}
else
{
Console.WriteLine("Error: Es wurde keine Zahl eingegeben.");
Console.WriteLine("Die Anzahl der vorgeschlagenen Filme bleibt bei " + resultCount + ".");
}
foreach (int userId in userIdPredList)
{
Console.WriteLine("Vorgeschlagene Filme für User mit UserId " + userId);
IList <IRecommendedItem> recommendedItems = recommender.Recommend(userId, resultCount);
foreach (var recItem in recommendedItems)
{
Console.WriteLine("Item: " + recItem.GetItemID() + " (" + movies.First(x => x.movieId == recItem.GetItemID()).title + ") ===> " + recItem.GetValue());
}
Console.WriteLine();
}
}
else if (input == "P")
{
foreach (int userId in userIdPredList)
{
Console.WriteLine("Prediction für User mit Id" + userId + ":");
foreach (int movieId in movieIdPredList)
{
float predictionValue = recommender.EstimatePreference(userId, movieId);
Console.WriteLine(movieId + " -> " + movies.First(x => x.movieId == movieId).title + " ===> " + predictionValue);
}
Console.WriteLine();
}
}
else if (input == "UADD")
{
Console.WriteLine("Welcher Benutzer (Id) soll zur Liste für Vorschläge hinzugefügt werden?");
string userIdInput = Console.ReadLine();
if (int.TryParse(userIdInput, out int userIdParsed))
{
if (users.Contains(userIdParsed))
{
userIdPredList.Add(userIdParsed);
}
else
{
Console.WriteLine("Error: UserId existiert nicht.");
}
}
else
{
Console.WriteLine("Error: Es wurde keine Zahl eingegeben.");
}
}
else if (input == "MADD")
{
Console.WriteLine("Welcher Film (Id) soll zur Liste für Vorschläge hinzugefügt werden?");
string movieIdInput = Console.ReadLine();
if (int.TryParse(movieIdInput, out int movieIdParsed))
{
bool movieIdFound = false;
foreach (MovieRecord movie in movies)
{
if (movie.movieId == movieIdParsed)
{
movieIdPredList.Add(movieIdParsed);
movieIdFound = true;
break;
}
}
if (!movieIdFound)
{
Console.WriteLine("Error: movieId existiert nicht. Es wurde kein Film zur Liste hinzugefügt");
}
}
else
{
Console.WriteLine("Error: Es wurde keine Zahl eingegeben.");
}
}
else if (input == "UCLEAR")
{
userIdPredList.Clear();
}
else if (input == "MCLEAR")
{
movieIdPredList.Clear();
}
}
Console.WriteLine("Programm beendet");
Console.ReadLine();
}
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());
}
}
