@Override
protected void calculate(Dataset dataSet) {
int nItems = dataSet.getItemCount();
int nRatingValues = 5;
/*
* The penalties distort the scale that we use for similarities
* maxBoundWeight is an auxiliary variable for scaling back to [0,1]
*/
double scaleFactor = 0.0;
similarityValues = new double[nItems][nItems];
if (keepRatingCountMatrix) {
ratingCountMatrix = new RatingCountMatrix[nItems][nItems];
}
// if we want to use mapping from itemId to index then generate
// index for every itemId
if (useObjIdToIndexMapping) {
for (Item item : dataSet.getItems()) {
idMapping.getIndex(String.valueOf(item.getId()));
}
}
// By using these variables we reduce the number of method calls
// inside the double loop.
int totalCount = 0;
int agreementCount = 0;
for (int u = 0; u < nItems; u++) {
int itemAId = getObjIdFromIndex(u);
Item itemA = dataSet.getItem(itemAId);
// we only need to calculate elements above the main diagonal.
for (int v = u + 1; v < nItems; v++) {
int itemBId = getObjIdFromIndex(v);
Item itemB = dataSet.getItem(itemBId);
RatingCountMatrix rcm = new RatingCountMatrix(itemA, itemB, nRatingValues);
totalCount = rcm.getTotalCount();
agreementCount = rcm.getAgreementCount();
if (agreementCount > 0) {
/*
* See ImprovedUserBasedSimilarity class for detailed
* explanation.
*/
double weightedDisagreements = 0.0;
int maxBandId = rcm.getMatrix().length - 1;
for (int matrixBandId = 1; matrixBandId <= maxBandId; matrixBandId++) {
/*
* The following is a heuristic. Can you figure out what characteristics
* are captured in such an expression? The numbers 1.8 and 0.4 are arbitrary,
* however, we could define them by solving an optimization problem.
* How would you formulate the problem? How would you solve it?
*/
double bandWeight = 1.8 - Math.exp(1 - matrixBandId);
bandWeight = Math.pow(bandWeight, 0.4);
if (bandWeight > scaleFactor) {
scaleFactor = bandWeight;
}
weightedDisagreements += bandWeight * rcm.getBandCount(matrixBandId);
}
double similarityValue = 1.0 - (weightedDisagreements / totalCount);
// w is the upper (negative) bound of the weighted similarity scale
double w = scaleFactor * (totalCount - agreementCount);
similarityValues[u][v] = (w + similarityValue) / (w + 1);
} else {
similarityValues[u][v] = 0.0;
}
if (keepRatingCountMatrix) {
ratingCountMatrix[u][v] = rcm;
}
}
// for u == v assign 1
// ratingCountMatrix wasn't created for this case
similarityValues[u][u] = 1.0;
}
}
public ItemPenaltyBasedSimilarity(String id, Dataset dataSet, boolean keepRatingCountMatrix) {
this.id = id;
this.keepRatingCountMatrix = keepRatingCountMatrix;
this.useObjIdToIndexMapping = dataSet.isIdMappingRequired();
calculate(dataSet);
}
