public static ScoreResults Score(this IRecommender classifier, UserBehaviorDatabase db, IRater rater)
{
UserBehaviorTransformer ubt = new UserBehaviorTransformer(db);
UserArticleRatingsTable actualRatings = ubt.GetUserArticleRatingsTable(rater);
var distinctUserArticlePairs = db.UserActions.GroupBy(x => new { x.UserID, x.ArticleID }).ToList();
double score = 0.0;
int count = 0;
foreach (var userArticle in distinctUserArticlePairs)
{
int userIndex = actualRatings.UserIndexToID.IndexOf(userArticle.Key.UserID);
int articleIndex = actualRatings.ArticleIndexToID.IndexOf(userArticle.Key.ArticleID);
double actualRating = actualRatings.Users[userIndex].ArticleRatings[articleIndex];
if (actualRating != 0)
{
double predictedRating = classifier.GetRating(userArticle.Key.UserID, userArticle.Key.ArticleID);
score += Math.Pow(predictedRating - actualRating, 2);
count++;
}
}
if (count > 0)
{
score = Math.Sqrt(score / count);
}
return(new ScoreResults(score));
}
public void Train(UserBehaviorDatabase db)
{
foreach (IRecommender classifier in classifiers)
{
classifier.Train(db);
}
}
public List <Suggestion> GetFirstSuggestions(UserBehaviorDatabase db, Databases.DomainModel.User user, int numSuggestions)
{
var userActionGroup = db.UserActions
.GroupBy(x => new { x.UserID })
.Select(g => new { g.Key.UserID })
.ToList();
List <Suggestion> suggestions = new List <Suggestion>();
MongodbFunctions mongo = new MongodbFunctions();
foreach (var a in userActionGroup)
{
Databases.DomainModel.User u = mongo.GetUser(a.UserID);
if (u.Gender.Equals(user.Gender) || u.BirthDate.Year == user.BirthDate.Year)
{
int userIndex = ratings.UserIndexToID.IndexOf(u.Id);
List <int> products = GetHighestRatedProductsForUser(userIndex).Take(3).ToList();
foreach (int productIndex in products)
{
ObjectId productId = ratings.ProductIndexToID[productIndex];
suggestions.Add(new Suggestion(u.Id, productId, ratings.Users[userIndex].ProductRatings[productIndex]));
}
}
}
suggestions.Sort((c, n) => n.Rating.CompareTo(c.Rating));
return(suggestions.Take(numSuggestions).ToList());
}
private void bgWorker_DoWork(object sender, DoWorkEventArgs e)
{
UserBehaviorDatabaseParser parser = new UserBehaviorDatabaseParser();
UserBehaviorDatabase db = parser.LoadUserBehaviorDatabase(e.Argument as string);
recommender.Train(db);
recommender.Save(savedModel);
}
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 void Train(UserBehaviorDatabase db)
{
UserBehaviorTransformer ubt = new UserBehaviorTransformer(db);
ratings = ubt.GetUserProductRatingsTable(rater);
List <ProductCategoryCount> productCategories = ubt.GetProductCategoryCounts();
ratings.AppendProductFeatures(productCategories);
FillTransposedRatings();
}
public void Train(UserBehaviorDatabase db)
{
UserBehaviorTransformer ubt = new UserBehaviorTransformer(db);
ratings = ubt.GetUserArticleRatingsTable(rater);
List <ArticleTagCounts> articleTags = ubt.GetArticleTagCounts();
ratings.AppendArticleFeatures(articleTags);
FillTransposedRatings();
}
public static TestResults Test(this IRecommender classifier, UserBehaviorDatabase db, int numSuggestions)
{
// We're only using the ratings to check for existence of a rating, so we can use a simple rater for everything
SimpleRater rater = new SimpleRater();
UserBehaviorTransformer ubt = new UserBehaviorTransformer(db);
UserArticleRatingsTable ratings = ubt.GetUserArticleRatingsTable(rater);
int correctUsers = 0;
double averagePrecision = 0.0;
double averageRecall = 0.0;
// Get a list of users in this database who interacted with an article for the first time
List <int> distinctUsers = db.UserActions.Select(x => x.UserID).Distinct().ToList();
var distinctUserArticles = db.UserActions.GroupBy(x => new { x.UserID, x.ArticleID });
// Now get suggestions for each of these users
foreach (int user in distinctUsers)
{
List <Suggestion> suggestions = classifier.GetSuggestions(user, numSuggestions);
bool foundOne = false;
int userIndex = ratings.UserIndexToID.IndexOf(user);
int userCorrectArticles = 0;
int userTotalArticles = distinctUserArticles.Count(x => x.Key.UserID == user);
foreach (Suggestion s in suggestions)
{
int articleIndex = ratings.ArticleIndexToID.IndexOf(s.ArticleID);
// If one of the top N suggestions is what the user ended up reading, then we're golden
if (ratings.Users[userIndex].ArticleRatings[articleIndex] != 0)
{
userCorrectArticles++;
if (!foundOne)
{
correctUsers++;
foundOne = true;
}
}
}
averagePrecision += (double)userCorrectArticles / numSuggestions;
averageRecall += (double)userCorrectArticles / userTotalArticles;
}
averagePrecision /= distinctUsers.Count;
averageRecall /= distinctUsers.Count;
return(new TestResults(distinctUsers.Count, correctUsers, averageRecall, averagePrecision));
}
public void Train(UserBehaviorDatabase db)
{
UserBehaviorTransformer ubt = new UserBehaviorTransformer(db);
ratings = ubt.GetUserArticleRatingsTable(rater);
SingularValueDecomposition factorizer = new SingularValueDecomposition(numFeatures, learningIterations);
svd = factorizer.FactorizeMatrix(ratings);
numUsers = ratings.UserIndexToID.Count;
numArticles = ratings.ArticleIndexToID.Count;
}
public void Train(UserBehaviorDatabase db)
{
UserBehaviorTransformer ubt = new UserBehaviorTransformer(db);
ratings = ubt.GetUserArticleRatingsTable(rater);
if (latentUserFeatureCount > 0)
{
SingularValueDecomposition svd = new SingularValueDecomposition(latentUserFeatureCount, 100);
SvdResult results = svd.FactorizeMatrix(ratings);
ratings.AppendUserFeatures(results.UserFeatures);
}
}
public void Train(UserBehaviorDatabase db)
{
UserBehaviorTransformer ubt = new UserBehaviorTransformer(db);
ratings = ubt.GetUserArticleRatingsTable(rater);
List <ArticleTagCounts> articleTags = ubt.GetArticleTagCounts();
//train article
List <ArticleAndTag> articles1 = ubt.Angualr2();
ratings.AppendArticleFeatures(articleTags);
// ratings.suggestArticle(articles1, 3);
FillTransposedRatings();
}
public void Train(UserBehaviorDatabase db)
{
UserBehaviorTransformer ubt = new UserBehaviorTransformer(db);
ratings = ubt.GetUserArticleRatingsTable(rater);
ratings.art = ubt.Angualr2();
List <ArticleAndTag> articles1 = ubt.Angualr2();
//if (latentUserFeatureCount > 0)
//{
// SingularValueDecomposition svd = new SingularValueDecomposition(latentUserFeatureCount, 100);
// SvdResult results = svd.FactorizeMatrix(ratings);
// ratings.AppendUserFeatures(results.UserFeatures);
//}
}
public void Train(UserBehaviorDatabase db)
{
}
public void Train(UserBehaviorDatabase db)
{
UserBehaviorTransformer ubt = new UserBehaviorTransformer(db);
ratings = ubt.GetUserProductRatingsTable(rater);
}
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));
}
}
