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 IRecommender BuildRecommender(IDataModel model)
{
IUserSimilarity similarity = new TanimotoCoefficientSimilarity(model);
IUserNeighborhood neighborhood =
new NearestNUserNeighborhood(10, similarity, model);
return
(new GenericUserBasedRecommender(model, neighborhood, similarity));
}
public List <Historial> SelectRecommendations(string userId)
{
Data.dsMantenimientoTableAdapters.HistorialTableAdapter adapter = new Data.dsMantenimientoTableAdapters.HistorialTableAdapter();
Data.dsMantenimiento.HistorialDataTable dt = adapter.SelectHistorial();
var Recomendaciones = new List <Historial>();
if (dt.Rows.Count == 0)
{
return(Recomendaciones);
}
foreach (Data.dsMantenimiento.HistorialRow data in dt)
{
var Historial = new Historial();
Historial.Restaurante = data.Restaurante;
Historial.Usuario = data.Usuario;
Historial.Puntuacion = data.Puntuacion.ToString();
Recomendaciones.Add(Historial);
}
//var model = new FileDataModel("data.csv");
//var similarity = new TanimotoCoefficientSimilarity(model);
//var neighborhood = new NearestNUserNeighborhood(3, similarity, model);
//var recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
//var recommendedItems = recommender.Recommend(Convert.ToInt64(userId), 5);
var ordersDataModel = LoadOrdersDataModel();
// lets assume that we have a new user interted in some product
var currentProductID = 57; // Product Name: Ravioli Angelo
var modelWithCurrentUser = GetDataModelForNewUser(ordersDataModel, currentProductID);
var similarity = new TanimotoCoefficientSimilarity(modelWithCurrentUser);
// in this example, we have no preference values (scores)
// to get correct results 'BooleanfPref' recommenders should be used
var recommender = new GenericBooleanPrefItemBasedRecommender(modelWithCurrentUser, similarity);
var recommendedItems = recommender.Recommend(PlusAnonymousUserDataModel.TEMP_USER_ID, 10, null);
Console.WriteLine("Products similar to {0} ({1}):", currentProductID, GetProductName(currentProductID));
foreach (var ri in recommendedItems)
{
Console.WriteLine("\tProductID={0}: {1}", ri.GetItemID(), GetProductName(ri.GetItemID()));
}
Console.WriteLine("Press any key to continue...");
Console.ReadKey();
return(Recomendaciones);
}
