Building a Recommendation Engine: The XELOPES Library - Realtime Data Mining - page 293

* @param crecs the current recommendation indexes
* @param crewardValue the current reward value
* @throws MiningException
*/
private void learn(State cstate, int[] crecs, double
crewardValue)
throws MiningException {
String cStateStr ¼ (String) cstate.getValueCategory(0).
getValue();

// Create sample vector:
if (nextState ! ¼ null) {
String nextStateStr ¼ (String) nextState.getValue-
Category(0).getValue();
Action

¼

action recoEnv.getAction(nextStateStr,

cStateStr);
if (action ¼¼ null) {
// ...create new rule ...///

}
nextSelRec ¼ (int) action.getIndex();
}
if (state ! ¼ null) {
Action

¼

action new ComposedAction(recs, selRec,

recoEnv.recoIDAtt);
Reward reward ¼ new Reward(crewardValue);
Action nextAction ¼
new ComposedAction(nextRecs, nextSelRec, recoEnv.

recoIDAtt);
RecommVector recommVec ¼
new RecommVector(agent, state, action, reward,

nextState, nextAction);
agent.learnApply(recommVec);

}

// Update values:
boolean absorbing ¼ cStateStr.equals("_a_");
if (absorbing) {
state ¼ null;
recs ¼ null;
nextSelRec ¼ -1;

}
else {
state ¼ nextState;
recs

¼ nextRecs;

}