public static FactorizationMatrix newRandomMatrix(Users users, Items items, int K, double miu)
{
RandomGenerator randomGenerator = new RandomGenerator();
Dictionary <User, double> buVector = randomGenerator.newRandomVector <Users, User>(users, 0.1, -0.05);
Dictionary <Item, double> biVector = randomGenerator.newRandomVector <Items, Item>(items, 0.1, -0.05);
Matrix <User> p = randomGenerator.newRandomMatrix <Users, User>(users, 0.1, -0.05, K);
Matrix <Item> q = randomGenerator.newRandomMatrix <Items, Item>(items, 0.1, -0.05, K);
FactorizationMatrix model = new FactorizationMatrix(miu, buVector, biVector, p, q);
return(model);
}
public void Train()
{
Console.WriteLine("***************** Train Matrix Factorization Model *********************");
mf = FactorizationMatrix.newRandomMatrix(trainUsers, trainItems, cFeatures, miu);
double error;
double actualRating;
double predictedRating;
double prevRmse = Double.MaxValue;
EvaluationEngine evaluationEngine = new EvaluationEngine(miu);
double currRmse = evaluationEngine.computeRMSE(validationUsers, validationItems, this);
int iteration = 1;
while (prevRmse > currRmse) //TODO Eyal - add num iteration and improvment threshold conditions
{
Console.WriteLine("Iteraion: {0}, Current RMSE: {1}", iteration, currRmse);
foreach (User user in trainUsers)
{
foreach (string itemId in user.GetRatedItems())
{
Item item = trainItems.GetItemById(itemId);
actualRating = user.GetRating(item.GetId());
predictedRating = mf.Predict(user, item);
error = actualRating - predictedRating;
var userBias = mf.getBu(user);
mf.setBu(user, userBias + yRate * (error - lambdaRate * userBias));
var itemBias = mf.getBi(item);
mf.setBi(item, itemBias + yRate * (error - lambdaRate * itemBias));
//update Qi and Pu
mf.UpdateQiAndPu(item, user, error);
}
}
prevRmse = currRmse;
currRmse = evaluationEngine.computeRMSE(validationUsers, validationItems, this);
iteration++;
}
Console.WriteLine(String.Format("Final Result - RMSE = {0}", prevRmse));
}