/** {@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;
}
/**
* Remove recommendations that have no chance of making it to the final selection, because their
* score will always be lower than the score of the last returned recommendation, even after post
* processing. This is a performance optimisation, so that irrelevant recommendations don't have
* to be post-processed.
*
* @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.
* @param recommendations computed so far.
*/
private void removeIrrelevant(
IN input, Context<OUT, IN> context, Recommendations<OUT> recommendations) {
float maxRelativeChange = maxRelativeChange(input, context);
if (Float.POSITIVE_INFINITY == maxRelativeChange) {
return;
}
int i = 0;
float minScoreInLimit = 0;
for (Recommendation<OUT> recommendation : recommendations.get(Integer.MAX_VALUE)) {
if (++i == context.config().limit()) {
minScoreInLimit = recommendation.getScore().getTotalScore() - maxRelativeChange;
} else if (i > context.config().limit()
&& recommendation.getScore().getTotalScore() < minScoreInLimit) {
recommendations.remove(recommendation.getItem());
}
}
}