/**
* Get the maximum value by which score difference between any two recommendations can be changed
* by post processing.
*
* @param input input to the recommendation engine. Typically the person or item recommendations
* are being computed for.
* @param context additional information about the recommendation process.
* @return maximum relative change. If unknown, {@link Float#POSITIVE_INFINITY} will be returned.
*/
private float maxRelativeChange(IN input, Context<OUT, IN> context) {
float result = 0f;
for (PostProcessor<OUT, IN> postProcessor : postProcessors) {
float posInfluence = postProcessor.maxPositiveScore(input, context);
float negInfluence = postProcessor.maxNegativeScore(input, context);
if (posInfluence < 0) {
throw new IllegalStateException(
postProcessor
+ " has a negative influence score ("
+ posInfluence
+ "), should not be negative");
}
if (negInfluence > 0) {
throw new IllegalStateException(
postProcessor
+ " has a positive influence score ("
+ negInfluence
+ "), should not be positive");
}
if (Float.isInfinite(negInfluence) || Float.isInfinite(posInfluence)) {
result = Float.POSITIVE_INFINITY;
break;
}
result += Math.abs(negInfluence) + Math.abs(posInfluence);
}
return result;
}
/** {@inheritDoc} */
@Override
public Recommendations<OUT> doRecommend(IN input, Context<OUT, IN> context) {
Recommendations<OUT> recommendations = new Recommendations<>();
for (RecommendationEngine<OUT, IN> engine : engines) {
if (engine.participationPolicy(context).participate(input, context, recommendations)) {
recommendations.merge(engine.recommend(input, context));
}
}
removeIrrelevant(input, context, recommendations);
for (PostProcessor<OUT, IN> postProcessor : postProcessors) {
postProcessor.postProcess(recommendations, input, context);
}
return recommendations;
}