Building a Recommendation Engine: The XELOPES Library - Realtime Data Mining - page 281

SarsaVector , it requires a special action object ComposedAction to store
the actions. The reason is that we may have multiple recommendations as action.
ComposedAction extends Action and has a dual function: it represents a unique,
composed action and at the same time stores all the single actions, i.e., the
recommended products. At this, each attribute of ComposedAction represents a
single recommendation. The index of ComposedAction stores the selected action,
i.e., the “recommendation” that actually has been accepted.
RecommVector owns the method

public RecommVector toSelectedRecommVector() throws

MiningException;
which transforms the recommendation vector into a new instance which only
contains the selected actions instead of all recommended ones, i.e., the new
recommendation vector stores the real transitions of the SARSA tuple as in
Fig. 3.7 .
Finally, we have the abstract class RecommEnvironment that extends Environ-
ment of the RL package and implements StateBasedActionSet , an interface
reflecting the isomorphism between states and actions (4.1). Further, RecommEn-
vironment contains methods for the mapping between states/actions and their
indexes and a special handling for absorbing states. Additionally, RecommEn-
vironment provides methods to access transition probabilities.

Example 12.23 We consider our small test shop of Example 5.4. To make the case
more realistic, we further assume that in the course of the sessions, recommenda-
tions are displayed. We select the DP Algorithm of Sect. 5.2 . In the following, we
describe an implementation of a simple recommendation engine by means of the
Recomm package. We start with the overall execution method:

/** The environment object. */
protected RecommEngineEnvironment recoEnv ¼ null;

/** The recommendation agent. */
protected RecommAgent agent ¼ null;

/**
* Run the recommendation engine example.
*
* @throws MiningException error while example is running
*/
public void run() throws MiningException {

// Create recommendation environment:
recoEnv ¼ new RecommEngineEnvironment();
recoEnv.init(null);

// Create recommendation agent:
agent ¼ createDPRecommAgent();