public List <Suggestion> GetSuggest(UserBehavior db, long userId)
{
IRater rater = new SimpleRater();
IComparer comparer = new CorrelationUserComparer();
recommender = new ItemCollaborativeFilterRecommender(comparer, rater, 50);
recommender.Train(db);
var suggestion = recommender.GetSuggestions(userId, 500);
return(suggestion);
}
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));
}
private void bgRecommend_DoWork(object sender, DoWorkEventArgs e)
{
GetRecommendation args = e.Argument as GetRecommendation;
e.Result = recommender.GetSuggestions(args.UserID, args.Ratings);
}
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));
}
}
