Building a Recommendation Engine: The XELOPES Library - Realtime Data Mining - page 289

* @param cond conditional probability (else

unconditional)
* @param transP the transition probability at specified
(state, action) pair
* @throws MiningException
*/
public void setTransP(State state, Action action, boolean
cond, double transP)
throws MiningException {

//...Implementation ...//

}

// ---------------------------
// Other methods
// ---------------------------
...

}

Thus, we use the StateSet and ActionSet classes of the RL Core package to store
the products and rules. Of course, in real recommendation engine implementations,
this storage is implemented in a much more sophisticated way supporting large rule
sets based on hash mappings. The transition probabilities p ða n ( S , S 0 ) including their
step numbers n are stored by the hash tables dpHash and stepHash , respectively.
Again, we use a very simple implementation here which is also not very fast.
After we have designed the recommendation environment, we need to create the
recommendation agent.

/**
* Create DP recommendation agent.
*
* @return the agent object
* @throws MiningException
*/
private

RecommAgent createDPRecommAgent() throws

MiningException {

// Create settingsobject:
DPRecommAgentSettings agentSettings ¼ new DPRecommAgen-
tSettings();
agentSettings.setInputDataSpecification(new MiningDa-

taSpecification("dummy"));
agentSettings.setDpVersionSubtype(PRecommAgen-
tSettings.P_VERSION_SUBTYPE_FULL);
agentSettings.setAlphaType(

RecommAgentSettings.

RL_STEP_SIZE_FIXED );
agentSettings.setAlpha( 0.1 );