Could someone help me out with identifying the A. Item-Based collaborative filtering pieces of code and the B. User-Based collaborative filtering pieces of code? --------------------------------------------------------------------------------------------------------------- 1. MeanSquaredDifferenceMetric.java:

@Override

public double getSimilarity(final Profile p1, final Profile p2) {

Set commonIds = p1.getCommonIds(p2);

double sumSquaredDifference = 0.0;

for (Integer id : commonIds) {

double diff = p1.getValue(id) - p2.getValue(id);

sumSquaredDifference += diff * diff;

}

double similarity = 1.0 - (sumSquaredDifference / commonIds.size()) / ((rmax - rmin) * (rmax - rmin));

return similarity;

}

----------------------------------------------------------------------------------------------------------------------------------------

2. thresholdNeighbourhood.java:

public void computeNeighbourhoods(final SimilarityMap simMap)

{

for (T id : simMap.getIds())

{

Profile profile = simMap.getSimilarities(id);

Set neighbourIds = profile.getIdsWithValueAbove(threshold);

for (T neighbourId : neighbourIds)

{

this.add(id, neighbourId);

}

}

}

---------------------------------------------------------------------------------------------------------------------------------------- 3. DeviationFromMeanUserPredictor.java:

public Double getPrediction(final UBCFRatingPredictionAlg alg,

final Integer userId, final Integer itemId)

{

Profile userProfile = alg.getTrainingData().getProfile(userId);

if (userProfile == null)

{

return null;

}

Set commonIds = userProfile.getCommonIds(alg.getTrainingData().getProfile(itemId));

if (commonIds.isEmpty())

{

return null;

}

double sumDeviation = 0.0;

int count = 0;

for (Integer id : commonIds)

{

double deviation = userProfile.getValue(id) - userProfile.getMeanValue();

sumDeviation += deviation;

count++;

}

if (count == 0)

{

return null;

}

double predictedRating = userProfile.getMeanValue() + (sumDeviation / count);

return predictedRating;

} ---------------------------------------------------------------------------------------------------------------------------------------- 4. SimSVD.java

@Override

public void learnItemSimilarity() {

int n = R.getColumnDimension();

// Create a copy of the rating matrix R to use in the SVD algorithm

RealMatrix RCopy = R.copy();

// Subtract the global mean from each rating

double globalMean = DatasetReader.getGlobalMean(R);

for (int i = 0; i < RCopy.getRowDimension(); i++) {

for (int j = 0; j < RCopy.getColumnDimension(); j++) {

double val = RCopy.getEntry(i, j);

if (val > 0) {

RCopy.setEntry(i, j, val - globalMean);

}

}

}

// Perform SVD on the modified rating matrix RCopy

SingularValueDecomposition svd = new SingularValueDecomposition(RCopy);

// Get the left singular vectors (U matrix) and singular values (S matrix)

RealMatrix U = svd.getU();

RealMatrix S = svd.getS();

// Compute the item-item similarity matrix B using the U and S matrices

for (int i = 0; i < n; i++) {

for (int j = i + 1; j < n; j++) {

double dotProduct = U.getColumnVector(i).dotProduct(U.getColumnVector(j));

double cosineSimilarity = dotProduct / (S.getEntry(i, i) * S.getEntry(j, j));

B[i][j] = cosineSimilarity;

B[j][i] = cosineSimilarity;

}

}

// Regularize the similarity matrix B

for (int i = 0; i < n; i++) {

double diag = B[i][i];

double regularizer = K * mu + (1 - mu) * diag;

for (int j = 0; j < n; j++) {

if (i != j && B[i][j] != 0) {

B[i][j] = (K * B[i][j] + (1 - K) * diag) / regularizer;

}

}

}

} ----------------------------------------------------------------------------------------------------------------------------------------

5. Ease.java @Override

public void learnItemSimilarity() {

int n = R.getColumnDimension();

double[] itemMeans = new double[n];

double[] itemVariances = new double[n];

// Compute the mean and variance of each item's ratings

for (int j = 0; j < n; j++) {

double[] col = R.getColumn(j);

itemMeans[j] = DatasetReader.getMean(col);

itemVariances[j] = DatasetReader.getVariance(col, itemMeans[j]);

}

// Compute the item-item similarity matrix B using the EASE algorithm

for (int i = 0; i < n; i++) {

for (int j = i + 1; j < n; j++) {

double numerator = 0;

double denominator = 0;

for (int k = 0; k < R.getRowDimension(); k++) {

double rik = R.getEntry(k, i);

double rjk = R.getEntry(k, j);

if (rik > 0 && rjk > 0) {

double diff = rik - rjk;

numerator += diff * diff;

denominator += itemVariances[i] + itemVariances[j];

}

}