public void Train()
{
Console.WriteLine("***************** Train Stereotypes Model *********************");
stereotypes = newRandomCentroids(users, items, cStereotypes);
Users usersCopy = new Users(users);
bool isConverged = false;
int iteration = 1;
while (!isConverged && iteration <= MAX_ITERATION)
{
Console.WriteLine("Iteration: {0}", iteration);
List <User> prevCentroids = stereotypes.getStereotypesCentroids();
stereotypes.initStereotypesUsers();
List <User> usersWithNoSimilarity = new List <User>();;
foreach (User user in usersCopy)
{
//Calculate a users similarity to the stereotypes
List <KeyValuePair <User, double> > similarities = similarityEngine.calculateSimilarity(similarityMethod, user, prevCentroids, false, true); //TODO - check the similarity threshold for stereotype algorithm
//Determine users that don't have a correlation with non of the stereotypes
if (similarities.Count == 0)
{
usersWithNoSimilarity.Add(user);
continue;
}
//Allocate a user to it's most similar stereotype
User mostSimilarCentroid = similarities.Last().Key;
stereotypes.addUserToStereotype(mostSimilarCentroid.GetId(), user);
}
usersCopy.removeUsers(usersWithNoSimilarity);
//Determine convergence
stereotypes.reCalculateCentroids();
List <User> currCentroids = stereotypes.getStereotypesCentroids();
var prevAndCurrCentroidsSimilarity = currCentroids.Zip(prevCentroids, (x, y) => Tuple.Create(x, y)).Select(x => Tuple.Create(x.Item1, x.Item2, similarityEngine.calculateSimilarity(similarityMethod, x.Item1, x.Item2))).ToList();
if (prevAndCurrCentroidsSimilarity.All(x => x.Item3 > CENTROIDS_SIMILARITY_THRESHOLD))
{
isConverged = true;
}
else
{
iteration++;
}
}
}
public double Predict(User user, Item item)
{
double rating = 0;
List <User> centroids = stereotypes.getStereotypesCentroids();
string itemId = item.GetId();
if (!similarityDic.ContainsKey(user))
{
List <KeyValuePair <User, double> > similarities = similarityEngine.calculateSimilarity(similarityMethod, user, centroids, false, true);
if (similarities.Count == 0)
{
similarityDic.Add(user, null); //there is no similar centroid
rating = user.GetAverageRatings();
return(rating);
}
//add the most similar user to the dictionary
User mostSimilarUser = similarities.Last().Key;
similarityDic.Add(user, mostSimilarUser);
if (mostSimilarUser.GetRating(itemId) == 0) //if the most similar user didn't rate the item, return the average rating of that item
{
rating = item.GetAverageRatings();
return(rating);
}
}
//if we didn't find any similar centroid, the average rating of the user is return
if (similarityDic[user] == null)
{
return(user.GetAverageRatings());
}
var similarUserRating = similarityDic[user].GetRating(itemId);
return(similarUserRating == 0 ? item.GetAverageRatings() : similarUserRating);
}
