public MainForm()
{
InitializeComponent();
IRater rate = new LinearRater(-4, 2, 3, 1);
IComparer compare = new CorrelationUserComparer();
recommender = new UserCollaborativeFilterRecommender(compare, rate, 50);
if (File.Exists(savedModel))
{
try
{
recommender.Load(savedModel);
rtbOutput.Text = "Loaded model from file";
EnableForm(true);
}
catch
{
rtbOutput.Text = "Saved model is corrupt";
}
}
//RecommenderTests.TestAllRecommenders();
//RecommenderTests.FindBestRaterWeights();
}
public JsonResult GetSuggestions()
{
List <string> products = new List <string>();
if (User.Identity.IsAuthenticated && User.IsInRole("User"))
{
MongodbFunctions mongo = new MongodbFunctions();
Databases.DomainModel.User user = mongo.GetUser(User.Identity.Name);
RecommendationEngine.Interfaces.IRater rater = new LinearRater(1.0, 5.0);
RecommendationEngine.Interfaces.IComparer comparer = new CosineComparer();
RecommendationEngine.Recommenders.ItemCollaborativeFilterRecommender recommender1 = new RecommendationEngine.Recommenders.ItemCollaborativeFilterRecommender(comparer, rater, 20);
RecommendationEngine.Parsers.UserBehaviorDatabaseParser parser = new RecommendationEngine.Parsers.UserBehaviorDatabaseParser();
List <Databases.DomainModel.RecommenderAction> actions = parser.LoadForSearch();
RecommendationEngine.Parsers.UserBehaviorDatabase db = parser.LoadUserBehaviorDatabase(actions);
recommender1.Train(db);
List <RecommendationEngine.Objects.Suggestion> suggestions = new List <RecommendationEngine.Objects.Suggestion>();
List <Databases.DomainModel.Product> CategoryProducts = new List <Databases.DomainModel.Product>();
if (actions.Count(x => x.UserId.Equals(user.Id)) > 0)
{
suggestions = recommender1.GetSuggestions(user.Id, 5);
}
else
{
foreach (string subcat in user.Interests)
{
CategoryProducts.AddRange(mongo.GetCategoryProducts(subcat).Take(2));
}
foreach (Databases.DomainModel.Product p in CategoryProducts)
{
if (!products.Exists(x => x.Equals(p.Name)))
{
products.Add(p.Name);
}
}
suggestions = recommender1.GetFirstSuggestions(db, user, 5);
}
foreach (RecommendationEngine.Objects.Suggestion s in suggestions)
{
Databases.DomainModel.Product product = mongo.GetProduct(s.ProductID);
if (!products.Exists(x => x.Equals(product.Name)))
{
products.Add(product.Name);
}
}
}
return(Json(new { prods = products }, JsonRequestBehavior.AllowGet));
}
public static void FindBestRaterWeights()
{
using (StreamWriter w = new StreamWriter("rater-weights.csv", true))
{
w.WriteLine("down,up,view,dl,rmse,solved,total,precision,rank");
}
var once = new User(0, "");
var options = new List <dynamic>();
for (double up = 0.0; up < 5.0; up += 1)
{
for (double down = -5.0; down <= 0.0; down += 1)
{
for (double dl = 0.0; dl < 5.0; dl += 1)
{
for (double view = 0.0; view < 5.0; view += 1)
{
options.Add(new { up, down, dl, view });
}
}
}
}
var dbp = new UserBehaviorDatabaseParser();
var db = dbp.LoadUserBehaviorDatabase("UserBehaviour.txt");
var sp = new DaySplitter(db, 3);
var cp = new CorrelationUserComparer();
Parallel.ForEach(options, set =>
{
try
{
var rate = new LinearRater(set.down, set.up, set.view, set.dl);
var mfr = new MatrixFactorizationRecommender(20, rate);
//var mfr = new UserCollaborativeFilterRecommender(cp, rate, set.features);
mfr.Train(sp.TrainingDB);
var score = mfr.Score(sp.TestingDB, rate);
var results = mfr.Test(sp.TestingDB, 100);
lock (once)
{
using (StreamWriter w = new StreamWriter("rater-weights.csv", true))
{
w.WriteLine(set.down + "," + set.up + "," + set.view + "," + set.dl + "," + score.RootMeanSquareDifference + "," + results.UsersSolved + "," + results.TotalUsers + "," + results.AveragePrecision + "," + results.AverageRecall);
}
}
}
catch (Exception ex)
{
File.WriteAllText("errors.txt", ex.ToString());
}
});
}
public ActionResult Details(int id)
{
UserBehaviorDatabaseParser parser = new UserBehaviorDatabaseParser();
UserBehaviorDatabase db1 = parser.LoadUserBehaviorDatabase("/Data/NewBehavior.txt");
UserBehaviorTransformer ubt = new UserBehaviorTransformer(db1);
int userid = Convert.ToInt32(Session["id"].ToString());
string email = context.login.Where(m => m.Id == userid).FirstOrDefault().Email;
int realid = context.students.Where(m => m.Email == email).FirstOrDefault().Id;
string name = context.students.Where(m => m.Id == realid).FirstOrDefault().FirstName;
UplodedFile admin = context.ufiles.Find(id);
SimilarViewModel sam = new SimilarViewModel();
sam.Description = admin.Description;
sam.Id = admin.Id;
sam.Name = admin.Name;
sam.UpdatedFileName = admin.UpdatedFileName;
sam.UplodedBy = admin.UplodedBy;
sam.UplodedDate = admin.UplodedDate;
sam.path = context.ufiles.Where(m => m.Id == id).FirstOrDefault().Filename;
UserArticleRatingsTable ratings1;
IRater rate = new LinearRater(-4, 2, 0.5, 1);
ratings1 = ubt.GetUserArticleRatingsTable(rate);
List <SuggestedArticlePoints> SAT = ratings1.suggestArticle(ratings1.art, id);
List <UplodedFile> up = new List <UplodedFile>();
UplodedFile upa = new UplodedFile();
int i = 0;
List <double> p = new List <double>();
foreach (var item in SAT)
{
p.Add(item.Points);
upa = context.ufiles.Where(m => m.Id == item.ArticleId).FirstOrDefault();
up.Add(upa);
}
sam.point = p;
sam.uplodedFiles = up;
double a = sam.point[0];
var text = System.IO.File.ReadAllText("/Data/NewBehavior.txt");
List <string> lines = System.IO.File.ReadAllLines("/Data/NewBehavior.txt").ToList();
int index = text.IndexOf("# End");
text = text.Insert(index, "1,View," + realid + "," + name + "," + admin.Id + "," + admin.Name + Environment.NewLine);
System.IO.File.WriteAllText("/Data/NewBehavior.txt", text);
return(View(sam));
}
public static void TestAllRecommenders()
{
UserBehaviorDatabaseParser dbp = new UserBehaviorDatabaseParser();
UserBehaviorDatabase db = dbp.LoadUserBehaviorDatabase("UserBehaviour.txt");
//var ubt = new UserBehaviorTransformer(db);
//var uart = ubt.GetUserArticleRatingsTable();
//uart.SaveSparcityVisual("sparcity.bmp");
//uart.SaveUserRatingDistribution("distrib.csv");
//uart.SaveArticleRatingDistribution("distriba.csv");
var rate = new LinearRater();
var sp = new DaySplitter(db, 5);
var uc = new CorrelationUserComparer();
//var rr = new RandomRecommender();
//rr.Train(sp.TrainingDB);
//ScoreResults scores5 = rr.Score(sp.TestingDB, rate);
//TestResults results5 = rr.Test(sp.TestingDB, 30);
var ubc = new UserCollaborativeFilterRecommender(uc, rate, 30);
var mfr = new MatrixFactorizationRecommender(30, rate);
var icf = new ItemCollaborativeFilterRecommender(uc, rate, 30);
var hbr = new HybridRecommender(ubc, mfr, icf);
hbr.Train(sp.TrainingDB);
ScoreResults scores1 = hbr.Score(sp.TestingDB, rate);
TestResults results1 = hbr.Test(sp.TestingDB, 30);
ubc = new UserCollaborativeFilterRecommender(uc, rate, 30);
mfr = new MatrixFactorizationRecommender(30, rate);
icf = new ItemCollaborativeFilterRecommender(uc, rate, 30);
ubc.Train(sp.TrainingDB);
ScoreResults scores2 = ubc.Score(sp.TestingDB, rate);
TestResults results2 = ubc.Test(sp.TestingDB, 30);
mfr.Train(sp.TrainingDB);
ScoreResults scores3 = mfr.Score(sp.TestingDB, rate);
TestResults results3 = mfr.Test(sp.TestingDB, 30);
icf.Train(sp.TrainingDB);
ScoreResults scores4 = icf.Score(sp.TestingDB, rate);
TestResults results4 = icf.Test(sp.TestingDB, 30);
using (StreamWriter w = new StreamWriter("results.csv"))
{
w.WriteLine("model,rmse,users,user-solved,precision,recall");
w.WriteLine("UCF," + scores2.RootMeanSquareDifference + "," + results2.TotalUsers + "," + results2.UsersSolved + "," + results2.AveragePrecision + "," + results2.AverageRecall);
w.WriteLine("SVD," + scores3.RootMeanSquareDifference + "," + results3.TotalUsers + "," + results3.UsersSolved + "," + results3.AveragePrecision + "," + results3.AverageRecall);
w.WriteLine("ICF," + scores4.RootMeanSquareDifference + "," + results4.TotalUsers + "," + results4.UsersSolved + "," + results4.AveragePrecision + "," + results4.AverageRecall);
w.WriteLine("HR," + scores1.RootMeanSquareDifference + "," + results1.TotalUsers + "," + results1.UsersSolved + "," + results1.AveragePrecision + "," + results1.AverageRecall);
}
}
public ActionResult Index()
{
List <Databases.DomainModel.Product> products = new List <Databases.DomainModel.Product>();
MongodbFunctions mongo = new MongodbFunctions();
if (User.Identity.IsAuthenticated && User.IsInRole("User"))
{
TimescaledbFunctions tdb = new TimescaledbFunctions();
Databases.DomainModel.User user = mongo.GetUser(User.Identity.Name);
if (!tdb.NotificationSent(user.Id.ToString()) && (30000 - tdb.MonthShopping(user.Id.ToString())) < 3000)
{
Databases.DomainModel.Notification notification = new Databases.DomainModel.Notification
{
Content = "Poštovani, do ostvarivanja popusta od 20% ostalo Vam je manje od 3000 dinara.",
Title = "Mali iznos do popusta",
Date = DateTime.Now.Date,
Tag = "do_popusta",
Read = false,
User = new MongoDB.Driver.MongoDBRef("users", user.Id)
};
tdb.SendNotification(user.Id.ToString(), mongo.AddNotification(notification, user.Email).ToString(), "do_popusta");
}
//recommend za proizvode
RecommendationEngine.Interfaces.IRater rater = new LinearRater(1.0, 5.0);
RecommendationEngine.Interfaces.IComparer comparer = new CosineComparer();
RecommendationEngine.Recommenders.ItemCollaborativeFilterRecommender recommender1 = new RecommendationEngine.Recommenders.ItemCollaborativeFilterRecommender(comparer, rater, 3);
RecommendationEngine.Recommenders.UserCollaborativeFilterRecommender recommender2 = new RecommendationEngine.Recommenders.UserCollaborativeFilterRecommender(comparer, rater, 5);
RecommendationEngine.Parsers.UserBehaviorDatabaseParser parser = new RecommendationEngine.Parsers.UserBehaviorDatabaseParser();
List <Databases.DomainModel.RecommenderAction> actions = parser.LoadForHome();
RecommendationEngine.Parsers.UserBehaviorDatabase db = parser.LoadUserBehaviorDatabase(actions);
recommender1.Train(db);
recommender2.Train(db);
if (actions.Count(x => x.UserId.Equals(user.Id)) > 0)//if user has actions, recommendation can be done
{
List <RecommendationEngine.Objects.Suggestion> suggestions1 = recommender1.GetSuggestions(user.Id, 6);
List <RecommendationEngine.Objects.Suggestion> suggestions2 = recommender2.GetSuggestions(user.Id, 3);
foreach (RecommendationEngine.Objects.Suggestion s in suggestions1)
{
Databases.DomainModel.Product product = mongo.GetProduct(s.ProductID);
if (!products.Exists(x => x.Id.Equals(product.Id)))
{
products.Add(product);
}
}
foreach (RecommendationEngine.Objects.Suggestion s in suggestions2)
{
Databases.DomainModel.Product product = mongo.GetProduct(s.ProductID);
if (!products.Exists(x => x.Id.Equals(product.Id)))
{
products.Add(product);
}
}
}
else//no actions, recommendation using gender and birth date
{
List <Databases.DomainModel.Product> CategoryProducts = new List <Databases.DomainModel.Product>();
foreach (string subcat in user.Interests)
{
CategoryProducts.AddRange(mongo.GetCategoryProducts(subcat).Take(2));
}
List <RecommendationEngine.Objects.Suggestion> suggestions1 = recommender1.GetFirstSuggestions(db, user, 10);
foreach (RecommendationEngine.Objects.Suggestion s in suggestions1)
{
Databases.DomainModel.Product product = mongo.GetProduct(s.ProductID);
if (!products.Exists(x => x.Id.Equals(product.Id)))
{
products.Add(product);
}
}
foreach (Databases.DomainModel.Product p in CategoryProducts)
{
if (!products.Exists(x => x.Id.Equals(p.Id)))
{
products.Add(p);
}
}
}
//recommend za reklame
List <Databases.DomainModel.Advert> adverts = new List <Databases.DomainModel.Advert>();
actions = parser.LoadForAdverts();
db = parser.LoadUserBehaviorDatabase(actions);
recommender1.Train(db);
List <RecommendationEngine.Objects.Suggestion> suggestionsU = new List <RecommendationEngine.Objects.Suggestion>();
List <RecommendationEngine.Objects.Suggestion> suggestionsI = new List <RecommendationEngine.Objects.Suggestion>();
List <Databases.DomainModel.Product> categoryProducts = new List <Databases.DomainModel.Product>();
if (actions.Count(x => x.UserId.Equals(user.Id)) > 0)
{
suggestionsU = recommender2.GetSuggestions(user.Id, 5);
suggestionsI = recommender1.GetSuggestions(user.Id, 5);
}
else
{
foreach (string subcat in user.Interests)
{
categoryProducts.AddRange(mongo.GetCategoryProducts(subcat).Take(2));
}
foreach (Databases.DomainModel.Product p in categoryProducts)
{
foreach (Databases.DomainModel.Advert advert in mongo.GetAdverts(p.Subcategory))
{
if (!adverts.Exists(x => x.Id.Equals(advert.Id)))
{
adverts.Add(advert);
}
}
}
suggestionsI = recommender1.GetFirstSuggestions(db, user, 10);
}
foreach (RecommendationEngine.Objects.Suggestion s in suggestionsI)
{
Databases.DomainModel.Product product = mongo.GetProduct(s.ProductID);
foreach (Databases.DomainModel.Advert advert in mongo.GetAdverts(product.Subcategory))
{
if (!adverts.Exists(x => x.Id.Equals(advert.Id)))
{
adverts.Add(advert);
}
}
}
foreach (RecommendationEngine.Objects.Suggestion s in suggestionsU)
{
Databases.DomainModel.Product product = mongo.GetProduct(s.ProductID);
foreach (Databases.DomainModel.Advert advert in mongo.GetAdverts(product.Subcategory))
{
if (!adverts.Exists(x => x.Id.Equals(advert.Id)))
{
adverts.Add(advert);
}
}
}
ViewBag.Adverts = adverts;
//last seen
List <MongoDB.Bson.ObjectId> prodIds = tdb.LastSeen(user.Id.ToString());
List <Databases.DomainModel.Product> lastSeen = new List <Databases.DomainModel.Product>();
foreach (MongoDB.Bson.ObjectId id in prodIds)
{
lastSeen.Add(mongo.GetProduct(id));
}
tdb.CloseConnection();
ViewBag.LastSeen = lastSeen;
}
else //admin logged
{
products = mongo.GetProducts();
}
return(View(products));
}
public ActionResult Index(string search = "")
{
if (search == "")
{
int id = Convert.ToInt32(Session["id"].ToString());
string email = context.login.Where(m => m.Id == id).FirstOrDefault().Email;
int realid = context.students.Where(m => m.Email == email).FirstOrDefault().Id;
IRater rate = new LinearRater(-4, 2, 0.5, 1);
IComparer compare = new CorrelationUserComparer();
recommender = new UserCollaborativeFilterRecommender(compare, rate, 200);
UserBehaviorDatabaseParser parser = new UserBehaviorDatabaseParser();
UserBehaviorDatabase db1 = parser.LoadUserBehaviorDatabase("/Data/NewBehavior.txt");
UserBehaviorTransformer ubt = new UserBehaviorTransformer(db1);
recommender.Train(db1);
int userId;
int ratings;
userId = realid;
ratings = 2;
List <Suggestion> result = new List <Suggestion>();
List <RecomendedArticles> rem = new List <RecomendedArticles>();
List <Suggestion> result2 = new List <Suggestion>();
RecomendedArticles recom;
if (ratings >= 1 && ratings <= 100)
{
new GetRecommendation {
UserID = userId, Ratings = ratings
};
result = recommender.GetSuggestions(userId, ratings);
result2 = recommender.GetSuggestions(userId, 6);
}
foreach (Suggestion suggestion in result)
{
var ye = context.ufiles.Where(m => m.Id == suggestion.ArticleID).FirstOrDefault();
recom = new RecomendedArticles()
{
Name = ye.Name,
UpdatedFileName = ye.UpdatedFileName,
UplodedBy = ye.UplodedBy,
Description = ye.Description,
Filename = ye.Filename,
imagepath = ye.imagepath,
UplodedDate = ye.UplodedDate,
Rating = suggestion.Rating,
Id = ye.Id,
};
rem.Add(recom);
}
NRViewModel recomendedArticles = new NRViewModel();
recomendedArticles.uplodedFiles = context.ufiles.OrderByDescending(m => m.Id).Take(6).ToList();
recomendedArticles.RecomendedArticles = rem;
return(View(recomendedArticles));
}
else
{
NRViewModel recomendedArticles = new NRViewModel();
recomendedArticles.uplodedFiles = context.ufiles.OrderByDescending(m => m.Id).Where(m => m.Name.Contains(search)).Take(6).ToList();
if (recomendedArticles.uplodedFiles == null)
{
ViewBag.messagea = "no item found";
}
ViewBag.message = "search";
return(View(recomendedArticles));
}
}
