/// <summary>
/// The user-based KNN collaborative filtering described in paper:
/// Resnick, P., et al., "GroupLens: an open architecture for collaborative filtering of netnews", 1994.
/// Link: http://dx.doi.org/10.1145/192844.192905
/// </summary>
/// <param name="R_train"></param>
/// <param name="R_unknown"></param>
/// <param name="K"></param>
/// <returns></returns>
public static DataMatrix PredictRatings(DataMatrix R_train, DataMatrix R_unknown, SimilarityData neighborsByUser, int K)
{
// Debug
Debug.Assert(R_train.UserCount == R_unknown.UserCount);
Debug.Assert(R_train.ItemCount == R_unknown.ItemCount);
int cappedCount = 0, globalMeanCount = 0;
// This matrix stores predictions
DataMatrix R_predicted = new DataMatrix(R_unknown.UserCount, R_unknown.ItemCount);
// Basic statistics from train set
double globalMean = R_train.GetGlobalMean();
Vector<double> meanByUser = R_train.GetUserMeans();
Vector<double> meanByItem = R_train.GetItemMeans();
// Predict ratings for each test user
// Single thread appears to be very fast, parallel.foreach is unnecessary
Object lockMe = new Object();
Parallel.ForEach(R_unknown.Users, user =>
{
int indexOfUser = user.Item1;
RatingVector userRatings = new RatingVector(R_train.GetRow(indexOfUser));
RatingVector unknownRatings = new RatingVector(user.Item2);
Utils.PrintEpoch("Predicting user/total", indexOfUser, R_train.UserCount);
// Note that there are more than K neighbors in the list (sorted by similarity)
// we will use the top-K neighbors WHO HAVE RATED THE ITEM
// For example we have 200 top neighbors, and we hope there are
// K neighbors in the list have rated the item. We can't keep
// everyone in the neighbor list because there are too many for large data sets
var topNeighborsOfUser = neighborsByUser[indexOfUser];
//Dictionary<int, double> topKNeighbors = KNNCore.GetTopKNeighborsByUser(userSimilarities, indexOfUser, K);
double meanOfUser = meanByUser[indexOfUser];
// Loop through each ratingto be predicted
foreach (Tuple<int, double> unknownRating in unknownRatings.Ratings)
{
int itemIndex = unknownRating.Item1;
double prediction;
// TODO: we actually should use the Top-K neighbors
// that have rated this item, otherwise we may have
// only a few neighbors rated this item
// Compute the average rating on item iid given
// by the top K neighbors. Each rating is offsetted by
// the neighbor's average and weighted by the similarity
double weightedSum = 0;
double weightSum = 0;
int currentTopKCount = 0;
foreach (KeyValuePair<int, double> neighbor in topNeighborsOfUser)
{
int neighborIndex = neighbor.Key;
double similarityOfNeighbor = neighbor.Value;
double itemRatingOfNeighbor = R_train[neighborIndex, itemIndex];
// We count only if the neighbor has seen this item before
if (itemRatingOfNeighbor != 0)
{
weightSum += similarityOfNeighbor;
weightedSum += (itemRatingOfNeighbor - meanByUser[neighborIndex]) * similarityOfNeighbor;
currentTopKCount++;
if (currentTopKCount >= K) { break; } // Stop when we have seen K neighbors
}
}
// A zero weightedSum means this is a cold item and global mean will be assigned by default
if (weightedSum != 0)
{
prediction = meanOfUser + weightedSum / weightSum;
}
else
{
prediction = globalMean;
globalMeanCount++;
}
// Cap the ratings
if (prediction > Config.Ratings.MaxRating)
{
cappedCount++;
prediction = Config.Ratings.MaxRating;
}
if (prediction < Config.Ratings.MinRating)
{
cappedCount++;
prediction = Config.Ratings.MinRating;
}
lock (lockMe)
{
R_predicted[indexOfUser, itemIndex] = prediction;
}
}
});
Utils.PrintValue("# capped predictions", cappedCount.ToString("D"));
Utils.PrintValue("# default predictions", globalMeanCount.ToString("D"));
return R_predicted;
}
public static DataMatrix PredictRatings(PrefRelations PR_train,
DataMatrix R_unknown, int K, SimilarityData neighborsByUser)
{
Debug.Assert(PR_train.UserCount == R_unknown.UserCount);
Debug.Assert(PR_train.ItemCount == R_unknown.ItemCount);
// This matrix stores predictions
DataMatrix R_predicted = new DataMatrix(R_unknown.UserCount, R_unknown.ItemCount);
// This can be considered as the R_train in standard UserKNN
SparseMatrix positionMatrix = PR_train.GetPositionMatrix();
DataMatrix ratingMatrixFromPositions = new DataMatrix(positionMatrix);
Vector<double> meanByUser = ratingMatrixFromPositions.GetUserMeans();
Vector<double> meanByItem = ratingMatrixFromPositions.GetItemMeans();
double globalMean = ratingMatrixFromPositions.GetGlobalMean();
// Predict positions for each test user
// Appears to be very fast, parallel.foreach is unnecessary
foreach (Tuple<int, Vector<double>> user in R_unknown.Users)
{
int indexOfUser = user.Item1;
Vector<double> indexesOfUnknownRatings = user.Item2;
Utils.PrintEpoch("Predicting user/total", indexOfUser, PR_train.UserCount);
// Note that there are more than K neighbors in the list (sorted by similarity)
// we will use the top-K neighbors WHO HAVE RATED THE ITEM
// For example we have 200 top neighbors, and we hope there are
// K neighbors in the list have rated the item. We can't keep
// everyone in the neighbor list because there are too many for large data sets
var topNeighborsOfUser = neighborsByUser[indexOfUser];
double meanOfUser = meanByUser[indexOfUser];
// Loop through each position to be predicted
foreach (Tuple<int, double> unknownRating in indexesOfUnknownRatings.EnumerateIndexed(Zeros.AllowSkip))
{
int indexOfUnknownItem = unknownRating.Item1;
// Compute the position of this item for the user
// by combining neighbors' positions on this item
double weightedSum = 0;
double weightSum = 0;
int currentTopKCount = 0;
foreach (KeyValuePair<int, double> neighbor in topNeighborsOfUser)
{
int indexOfNeighbor = neighbor.Key;
double similarityOfNeighbor = neighbor.Value;
double itemPositionOfNeighbor = ratingMatrixFromPositions[indexOfNeighbor, indexOfUnknownItem];
// We count only if the neighbor has seen this item before
if (itemPositionOfNeighbor != 0)
{
// Recall that we use a constant to hold position value 0
// we revert it back here
if (itemPositionOfNeighbor == Config.ZeroInSparseMatrix)
{
Debug.Assert(true, "By using the PositionShift constant, we should not be in here.");
itemPositionOfNeighbor = 0;
}
weightSum += similarityOfNeighbor;
weightedSum += (itemPositionOfNeighbor - meanByUser[indexOfNeighbor]) * similarityOfNeighbor;
currentTopKCount++;
if(currentTopKCount>= K)
{
break;
}
}
}
// If any neighbor has seen this item
if (currentTopKCount != 0)
{
// TODO: Add user mean may improve the performance
R_predicted[indexOfUser, indexOfUnknownItem] = meanOfUser + weightedSum / weightSum;
}
else
{
R_predicted[indexOfUser, indexOfUnknownItem] = globalMean;
}
}
}//);
return R_predicted;
}
public static DataMatrix PredictRatings(DataMatrix R_train, DataMatrix R_unknown,
int maxEpoch, double learnRate, double regularization, int factorCount)
{
int userCount = R_train.UserCount;
int itemCount = R_train.ItemCount;
int ratingCount = R_train.NonZerosCount;
double meanOfGlobal = R_train.GetGlobalMean();
DataMatrix R_train_unknown = R_train.IndexesOfNonZeroElements(); // For testing convergence
// User latent vectors with default seed
Matrix<double> P = Utils.CreateRandomMatrixFromNormal(userCount, factorCount, 0, 0.1, Config.Seed);
// Matrix<double> P = Utils.CreateRandomMatrixFromUniform(userCount, factorCount, 0, 0.1, Config.Seed);
// Item latent vectors with a different seed
Matrix<double> Q = Utils.CreateRandomMatrixFromNormal(factorCount, itemCount, 0, 0.1, Config.Seed + 1);
//Matrix<double> Q = Utils.CreateRandomMatrixFromUniform(factorCount, itemCount, 0, 0.1, Config.Seed + 1);
// SGD
double e_prev = double.MaxValue;
for (int epoch = 0; epoch < maxEpoch; ++epoch)
{
foreach (Tuple<int, int, double> element in R_train.Ratings)
{
int indexOfUser = element.Item1;
int indexOfItem = element.Item2;
double rating = element.Item3;
double e_ij = rating - (meanOfGlobal + P.Row(indexOfUser).DotProduct(Q.Column(indexOfItem)));
// Update feature vectors
Vector<double> P_u = P.Row(indexOfUser);
Vector<double> Q_i = Q.Column(indexOfItem);
Vector<double> P_u_updated = P_u + (Q_i.Multiply(e_ij) - P_u.Multiply(regularization)).Multiply(learnRate);
P.SetRow(indexOfUser, P_u_updated);
Vector<double> Q_i_updated = Q_i + (P_u.Multiply(e_ij) - Q_i.Multiply(regularization)).Multiply(learnRate);
Q.SetColumn(indexOfItem, Q_i_updated);
#region Update feature vectors loop version
/*
// Update feature vectors
for (int k = 0; k < factorCount; ++k)
{
double factorOfUser = P[indexOfUser, k];
double factorOfItem = Q[k, indexOfItem];
P[indexOfUser, k] += learnRate * (e_ij * factorOfItem - regularization * factorOfUser);
Q[k, indexOfItem] += learnRate * (e_ij * factorOfUser - regularization * factorOfItem);
}
*/
#endregion
}
// Display the current regularized error see if it converges
double e_curr = 0;
if (epoch == 0 || epoch == maxEpoch - 1 || epoch % (int)Math.Ceiling(maxEpoch * 0.1) == 4)
{
Matrix<double> predictedMatrix = R_train_unknown.PointwiseMultiply(P.Multiply(Q));
SparseMatrix correctMatrix = R_train.Matrix;
double squaredError = (correctMatrix - predictedMatrix).SquaredSum();
double regularizationPenaty = regularization * (P.SquaredSum() + Q.SquaredSum());
double objective = squaredError + regularizationPenaty;
#region Linear implementation
/*
double e = 0;
foreach (Tuple<int, int, double> element in R_train.Ratings)
{
int indexOfUser = element.Item1;
int indexOfItem = element.Item2;
double rating = element.Item3;
e += Math.Pow(rating - P.Row(indexOfUser).DotProduct(Q.Column(indexOfItem)), 2);
for (int k = 0; k < factorCount; ++k)
{
e += (regularization / 2) * (Math.Pow(P[indexOfUser, k], 2) + Math.Pow(Q[k, indexOfItem], 2));
}
}
*/
#endregion
// Record the current error
e_curr = objective;
// Stop the learning if the regularized error falls below a certain threshold
if (e_prev - e_curr < 0.001)
{
Console.WriteLine("Improvment less than 0.001, learning stopped.");
break;
}
e_prev = e_curr;
Utils.PrintEpoch("Epoch", epoch, maxEpoch, "Objective cost", objective);
}
}
SparseMatrix R_predicted = new SparseMatrix(R_unknown.UserCount, R_unknown.ItemCount);
foreach(var element in R_unknown.Matrix.EnumerateIndexed(Zeros.AllowSkip))
{
int indexOfUser = element.Item1;
int indexOfItem = element.Item2;
double r_predicted = meanOfGlobal + P.Row(indexOfUser) * Q.Column(indexOfItem);
if (r_predicted > Config.Ratings.MaxRating) r_predicted = Config.Ratings.MaxRating;
if (r_predicted < Config.Ratings.MinRating) r_predicted = Config.Ratings.MinRating;
R_predicted[indexOfUser, indexOfItem] = r_predicted;
}
return new DataMatrix(R_predicted);
//return new RatingMatrix(R_unknown.PointwiseMultiply(P.Multiply(Q)));
}
/// <summary>
/// The user-based KNN collaborative filtering described in paper:
/// Resnick, P., et al., "GroupLens: an open architecture for collaborative filtering of netnews", 1994.
/// Link: http://dx.doi.org/10.1145/192844.192905
/// </summary>
/// <param name="R_train"></param>
/// <param name="R_unknown"></param>
/// <param name="K"></param>
/// <returns></returns>
public static DataMatrix PredictRatings(DataMatrix R_train, DataMatrix R_unknown, SimilarityData neighborsByUser, int K)
{
// Debug
Debug.Assert(R_train.UserCount == R_unknown.UserCount);
Debug.Assert(R_train.ItemCount == R_unknown.ItemCount);
int cappedCount = 0, globalMeanCount = 0;
// This matrix stores predictions
DataMatrix R_predicted = new DataMatrix(R_unknown.UserCount, R_unknown.ItemCount);
// Basic statistics from train set
double globalMean = R_train.GetGlobalMean();
Vector <double> meanByUser = R_train.GetUserMeans();
Vector <double> meanByItem = R_train.GetItemMeans();
// Predict ratings for each test user
// Single thread appears to be very fast, parallel.foreach is unnecessary
Object lockMe = new Object();
Parallel.ForEach(R_unknown.Users, user =>
{
int indexOfUser = user.Item1;
RatingVector userRatings = new RatingVector(R_train.GetRow(indexOfUser));
RatingVector unknownRatings = new RatingVector(user.Item2);
Utils.PrintEpoch("Predicting user/total", indexOfUser, R_train.UserCount);
// Note that there are more than K neighbors in the list (sorted by similarity)
// we will use the top-K neighbors WHO HAVE RATED THE ITEM
// For example we have 200 top neighbors, and we hope there are
// K neighbors in the list have rated the item. We can't keep
// everyone in the neighbor list because there are too many for large data sets
var topNeighborsOfUser = neighborsByUser[indexOfUser];
//Dictionary<int, double> topKNeighbors = KNNCore.GetTopKNeighborsByUser(userSimilarities, indexOfUser, K);
double meanOfUser = meanByUser[indexOfUser];
// Loop through each ratingto be predicted
foreach (Tuple <int, double> unknownRating in unknownRatings.Ratings)
{
int itemIndex = unknownRating.Item1;
double prediction;
// TODO: we actually should use the Top-K neighbors
// that have rated this item, otherwise we may have
// only a few neighbors rated this item
// Compute the average rating on item iid given
// by the top K neighbors. Each rating is offsetted by
// the neighbor's average and weighted by the similarity
double weightedSum = 0;
double weightSum = 0;
int currentTopKCount = 0;
foreach (KeyValuePair <int, double> neighbor in topNeighborsOfUser)
{
int neighborIndex = neighbor.Key;
double similarityOfNeighbor = neighbor.Value;
double itemRatingOfNeighbor = R_train[neighborIndex, itemIndex];
// We count only if the neighbor has seen this item before
if (itemRatingOfNeighbor != 0)
{
weightSum += similarityOfNeighbor;
weightedSum += (itemRatingOfNeighbor - meanByUser[neighborIndex]) * similarityOfNeighbor;
currentTopKCount++;
if (currentTopKCount >= K)
{
break;
} // Stop when we have seen K neighbors
}
}
// A zero weightedSum means this is a cold item and global mean will be assigned by default
if (weightedSum != 0)
{
prediction = meanOfUser + weightedSum / weightSum;
}
else
{
prediction = globalMean;
globalMeanCount++;
}
// Cap the ratings
if (prediction > Config.Ratings.MaxRating)
{
cappedCount++;
prediction = Config.Ratings.MaxRating;
}
if (prediction < Config.Ratings.MinRating)
{
cappedCount++;
prediction = Config.Ratings.MinRating;
}
lock (lockMe)
{
R_predicted[indexOfUser, itemIndex] = prediction;
}
}
});
Utils.PrintValue("# capped predictions", cappedCount.ToString("D"));
Utils.PrintValue("# default predictions", globalMeanCount.ToString("D"));
return(R_predicted);
}
