Skip to content

init regBias in GBPR #252

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
SunYatong merged 2 commits into from
Apr 10, 2018
Merged

init regBias in GBPR #252

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
2b01883
init regBias in GBPR
ChenSuL Apr 8, 2018
0a06171
Update GBPRRecommender.java
ChenSuL Apr 8, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 10 additions & 2 deletions core/src/main/java/net/librec/recommender/cf/ranking/GBPRRecommender.java
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -66,6 +66,8 @@ public class GBPRRecommender extends MatrixFactorizationRecommender {
protected void setup() throws LibrecException {
super.setup();

regBias = conf.getDouble("rec.bias.regularization", 0.01);

itemBiases = new DenseVector(numItems);
itemBiases.init();

Expand All @@ -78,7 +80,7 @@ protected void setup() throws LibrecException {
}

@Override
protected void trainModel() throws LibrecException {
protected void trainModel() throws LibrecException {
for (int iter = 1; iter <= numIterations; iter++) {

loss = 0.0d;
Expand Down Expand Up @@ -141,9 +143,11 @@ protected void trainModel() throws LibrecException {
// update bi, bj
double posBiasValue = itemBiases.get(posItemIdx);
itemBiases.add(posItemIdx, learnRate * (deriValue - regBias * posBiasValue));
loss += regBias * posBiasValue * posBiasValue;

double negBiasValue = itemBiases.get(negItemIdx);
itemBiases.add(negItemIdx, learnRate * (-deriValue - regBias * negBiasValue));
loss += regBias * negBiasValue * negBiasValue;

// update Pw
double averageWeight = 1.0 / groupSet.size();
Expand All @@ -158,6 +162,8 @@ protected void trainModel() throws LibrecException {
double deltaGroup = rho * averageWeight * posItemFactorValue + (1 - rho) * delta * posItemFactorValue - delta * negItemFactorValue;
tempUserFactors.add(groupUserIdx, factorIdx, learnRate * (deriValue * deltaGroup - regUser * groupUserFactorValue));

loss += regUser * groupUserFactorValue * groupUserFactorValue;

sumGroup[factorIdx] += groupUserFactorValue;
}
}
Expand All @@ -170,9 +176,11 @@ protected void trainModel() throws LibrecException {

double posDelta = rho * averageWeight * sumGroup[factorIdx] + (1 - rho) * userFactorValue;
tempItemFactors.add(posItemIdx, factorIdx, learnRate * (deriValue * posDelta - regItem * posItemFactorValue));

loss += regItem * posItemFactorValue * posItemFactorValue;

double negDelta = -userFactorValue;
tempItemFactors.add(negItemIdx, factorIdx, learnRate * (deriValue * negDelta - regItem * negItemFactorValue));
loss += regItem * negItemFactorValue * negItemFactorValue;
}
}

Expand Down