public RecommendationEngine()
{
categories = CheckinOperations.getCategorie();
places = CheckinOperations.getPlaces();
pCategories = CheckinOperations.getPlaceCategories();
aEng = new AnalyzeEngine();
}
public AnalyzeEngine(bool tf)
{
categoryPoints = new ConcurrentDictionary <string, double>();
this.Categorie = CheckinOperations.getCategorie();
pCategories = CheckinOperations.getPlaceCategories();
likeCategories = CheckinOperations.listLikeCategories();
}
/// <summary>
/// Thresholds
/// </summary>
/// <param name="Popularity"></param>
/// <param name="CategoryQuality"></param>
public Engine(double Popularity, double CategoryQuality)
{
PopularityThreshold = Popularity;
CategoryQualityThreshold = CategoryQuality;
places = CheckinOperations.getPlaces().ToList();
placeCategories = CheckinOperations.getPlaceCategories();
CATEGORIES = CheckinOperations.getCategorie();
UserCheckins = CheckinOperations.listCheckIns();
checkinUser = CheckinOperations.listCheckInsReturnedUser();
}
private Dictionary <string, int> getDefaultData()
{
Dictionary <string, int> data = new Dictionary <string, int>();
var categories = CheckinOperations.getCategorie();
foreach (var cat in categories)
{
data.Add(cat.NAME, 0);
}
return(data);
}
public string GenerateData(string arff)
{
string data = "@data\n";
var userList = CheckinOperations.getUsers();
List <ArffData> datas = new List <ArffData>();
foreach (var user in userList)
{
ArffData d = new ArffData();
d.Name = user.USERNAME;
d.Categorie = getDefaultData();
var checkins = CheckinOperations.getCheckIns(user.USERID);
foreach (var checkin in checkins)
{
var categories = CheckinOperations.getPlaceCategories(checkin.PLACEID.Value);
foreach (var catPlace in categories)
{
var cat = CheckinOperations.getCategory(catPlace.CATEGORIEID.Value);
d.Categorie[cat.NAME] += 1;
}
}
d.CheckinCount = checkins.Count;
datas.Add(d);
}
foreach (var d in datas)
{
string row = string.Empty;
row += d.Name + ",";
foreach (var cat in d.Categorie)
{
if (cat.Value == 0)
{
row += cat.Value.ToString().Replace(",", ".") + ",";
}
else
{
float result = ((float)cat.Value) / (float)d.CheckinCount;
row += result.ToString().Replace(",", ".") + ",";
}
}
row = row.Substring(0, row.Length - 1);
data += row + "\n";
}
arff = arff.Replace("@DATA", data + "\n\n");
return(arff);
}
public void _Load()
{
categoryPoints = new ConcurrentDictionary <string, double>();
this.Categorie = CheckinOperations.getCategorie();
Engine eng = new Engine(popularityThreshold, CategoryQualityThreshold);
List <string> list_lines = new List <string>();
Parallel.ForEach(Categorie, (cat) =>
{
var catPoint = eng.getCategoryPoint(cat.CATEGORIEID);
categoryPoints.AddOrUpdate(cat.NAME, catPoint, (key, oldvalue) => oldvalue + catPoint);
});
pCategories = CheckinOperations.getPlaceCategories();
likeCategories = CheckinOperations.listLikeCategories();
//var catPoint = eng.getCategoryPoint(896);
//categoryPoints.AddOrUpdate("Amphitheater", catPoint, (key, oldvalue) => oldvalue + catPoint);
}
public string GenerateAttibutes(string arff)
{
var categories = CheckinOperations.getCategorie();
string attributes = string.Empty;
attributes += "@ATTRIBUTE Name string\n";
foreach (var cat in categories)
{
string name = cat.NAME;
foreach (var item in replaceChar)
{
name = name.Replace(item, "");
}
attributes += "@ATTRIBUTE " + name.Replace(" ", "_") + " numeric\n";
}
arff = arff.Replace("@ATTRIBUTE", attributes + "\n\n");
return(arff);
}
private List <FenomenResult> getSimilarUsers(Dictionary <string, double> CategoryProfile)
{
//Fenomen Profilleri oluşturuluyor..
var FenomenProfiles = CheckinOperations.listCategoryProfile();
List <Dictionary <string, double> > fProfiles = new List <Dictionary <string, double> >();
string[] splitChars1 = { "#" };
string[] splitChars2 = { ":" };
foreach (var profile in FenomenProfiles)
{
Dictionary <string, double> profil = new Dictionary <string, double>();
var features = profile.FEATURES.Split(splitChars1, StringSplitOptions.RemoveEmptyEntries);
foreach (var f in features)
{
string[] feat = f.Split(splitChars2, StringSplitOptions.RemoveEmptyEntries);
string name = feat[0];
string value = feat[1];
profil.Add(name, Convert.ToDouble(value));
}
fProfiles.Add(profil);
}
var result = new List <FenomenResult>();
var userVector = aEng.VectorToList(CategoryProfile);
for (int i = 0; i < fProfiles.Count; i++)
{
var fenomenVector = aEng.VectorToList(fProfiles[i]);
var r = CosineSimilarity.GetCosineSimilarity(userVector, fenomenVector);
result.Add(new FenomenResult()
{
fenomenProfile = fProfiles[i],
SimilarityResult = r
});
}
result.Sort((emp1, emp2) => emp1.SimilarityResult.CompareTo(emp2.SimilarityResult));
return(result);
}
