private void checkMaxUsers(RecommenderSystem rs, int min, int max, List <RecommenderSystem.PredictionMethod> lMethods)
{
for (int i = min; i <= max; i++)
{
rs.SetMaxUsers(i);
var tmp = rs.ComputeHitRatio(lMethods, 0.95);
foreach (var item in tmp)
{
update(item.Key, item.Value, i, "Max Users");
}
}
}
private void checkSimilarity(RecommenderSystem rs, int min_minSimilarity, int max_minSimilarity, List <RecommenderSystem.PredictionMethod> lMethods)
{
for (int i = min_minSimilarity; i <= max_minSimilarity; i++)
{
double value = (double)i / 100.0;
rs.SetSimilaritySaf(value);
var tmp = rs.ComputeHitRatio(lMethods, 0.95);
foreach (var item in tmp)
{
update(item.Key, item.Value, value, "similarity");
}
}
}
/// <summary>
///
/// </summary>
/// <param name="rs"></param>
/// <param name="min_minSimilarity">in percents</param>
/// <param name="max_minSimilarity">in percents</param>
/// <param name="minMaxSimilarUsers"></param>
/// <param name="maxMaxSimilarUsers"></param>
public Dictionary <string, TestData> Run(RecommenderSystem rs, int min_minSimilarity, int max_minSimilarity, int minMaxSimilarUsers, int maxMaxSimilarUsers)
{
_res.Clear();
List <RecommenderSystem.PredictionMethod> lMethods = new List <RecommenderSystem.PredictionMethod>();
lMethods.Add(RecommenderSystem.PredictionMethod.Pearson);
lMethods.Add(RecommenderSystem.PredictionMethod.Cosine);
checkSimilarity(rs, min_minSimilarity, max_minSimilarity, lMethods);
checkMaxUsers(rs, minMaxSimilarUsers, maxMaxSimilarUsers, lMethods);
return(_res);
}
public List <Product> RecommendedProducts(int id)
{
RecommenderSystem recommender = new RecommenderSystem();
List <Product> recommendedProducts = recommender.GetRecommendedProducts(id);
foreach (var item in recommendedProducts)
{
item.Picture = null;
}
return(recommendedProducts);
}
public IHttpActionResult Preporuka(int userId)
{
RecommenderSystem rc = new RecommenderSystem();
List <int> p = rc.GetRecomended(userId).Select(x => x.Key).ToList();
foreach (var proizvodId in p)
{
if (!_db.GotoviProizvod.Find(proizvodId).VidljivostMobile)
{
p.Remove(proizvodId);
}
}
return(Ok(p));
}
static void Main(string[] args)
{
string resultModel;
do
{
Console.WriteLine("Which model would you like to execute? (1 = CF , 2 = Content Model)");
resultModel = Console.ReadLine();
if (resultModel != "1" && resultModel != "2")
{
Console.WriteLine("Please choose '1' or '2' only");
}
} while (resultModel != "1" && resultModel != "2");
string latentFeatures;
int latentFeatureNumber = 0;
bool validNumber = true;
do
{
Console.WriteLine("How Many latent features?");
latentFeatures = Console.ReadLine();
try
{
latentFeatureNumber = Convert.ToInt32(latentFeatures);
if (latentFeatureNumber <= 0)
{
validNumber = false;
Console.WriteLine("Please Enter positive number.");
}
}
catch (Exception)
{
validNumber = false;
Console.WriteLine("Insert positive number");
}
} while (validNumber == false);
m_recommenderSystem = new RecommenderSystem(latentFeatureNumber, resultModel == "1"?true:false);
}
