protected void init_spaces() {
userspace = new float[maxuid + 1][];
itemspace = new float[maxiid + 1][];
float frag =
(float) Math.sqrt((avgrating * 0.1f / factor)); // pu * qi is about 10% effect of r^ui
for (int u = 0; u < userspace.length; u++) {
userspace[u] = new float[factor];
for (int f = 0; f < factor; f++) {
userspace[u][f] = (float) (frag * (Utilities.randomDouble()));
}
}
System.out.println(itemspace.length);
for (int i = 0; i < itemspace.length; i++) {
itemspace[i] = new float[factor];
for (int f = 0; f < factor; f++) {
itemspace[i][f] = (float) (frag * (Utilities.randomDouble()));
}
}
userbias = new float[maxuid + 1];
itembias = new float[maxiid + 1];
for (int u = 0; u < userbias.length; u++) {
userbias[u] = (float) Utilities.randomDouble(-0.5, 0.5); //
}
for (int i = 0; i < itembias.length; i++) {
itembias[i] = (float) Utilities.randomDouble(-1.5, 1.5);
}
}
private void _train() throws Exception {
System.out.println("--------------------");
float learningSpeed = this.alpha;
for (int loop = 0; loop < this.loops; loop++) {
dataEntry.reOpen();
double totalError = 0;
int n = 0;
long timeStart = System.currentTimeMillis();
// core computation
for (Vector v = dataEntry.getNextVector(); v != null; v = dataEntry.getNextVector()) {
UserRatings ur = new UserRatings(v);
for (RatingInfo ri = ur.getNormalNextRating(); ri != null; ri = ur.getNormalNextRating()) {
float eui =
ri.rating
- (this.avgrating
+ userbias[ri.userId]
+ itembias[ri.itemId]
+ Utilities.innerProduct(userspace[ri.userId], itemspace[ri.itemId]));
// perform gradient on user/item bias
userbias[ri.userId] += learningSpeed * (eui - this.lambda * userbias[ri.userId]);
itembias[ri.itemId] += learningSpeed * (eui - this.lambda * itembias[ri.itemId]);
// perform gradient on pu/qi
for (int f = 0; f < this.factor; f++) {
userspace[ri.userId][f] =
userspace[ri.userId][f]
+ learningSpeed
* (eui * itemspace[ri.itemId][f] - this.lambda * userspace[ri.userId][f]);
itemspace[ri.itemId][f] =
itemspace[ri.itemId][f]
+ learningSpeed
* (eui * userspace[ri.userId][f] - this.lambda * itemspace[ri.itemId][f]);
}
totalError += Math.abs(eui);
n += 1;
}
}
long timeSpent = System.currentTimeMillis() - timeStart;
learningSpeed *= this.convergence;
System.out.println(
String.format(
"loop:%d\ttime(ms):%d\tavgerror:%.6f\tnext alpha:%.5f",
loop, timeSpent, (totalError / n), learningSpeed));
// System.out.print("loop " + loop + " finished~ Time spent: " + (timeSpent / 1000.0) + "
// next speed :" + learningSpeed);
// System.out.println(" total training ratings = " + n);
}
dataEntry.close();
}
protected float predict(int userId, int itemId) {
return this.avgrating
+ userbias[userId]
+ itembias[itemId]
+ Utilities.innerProduct(userspace[userId], itemspace[itemId]);
}
