private float doEstimatePreference(long userID, long itemID) {
buildAveragesLock.readLock().lock();
try {
RunningAverage itemAverage = itemAverages.get(itemID);
if (itemAverage == null) {
return Float.NaN;
}
RunningAverage userAverage = userAverages.get(userID);
if (userAverage == null) {
return Float.NaN;
}
double userDiff = userAverage.getAverage() - overallAveragePrefValue.getAverage();
return (float) (itemAverage.getAverage() + userDiff);
} finally {
buildAveragesLock.readLock().unlock();
}
}
@Test
public void testEvaluate() throws Exception {
DataModel model = getDataModel();
Recommender recommender1 = new SlopeOneRecommender(model);
Recommender recommender2 = new ItemAverageRecommender(model);
RecommenderEvaluator evaluator = new PreferenceBasedRecommenderEvaluator();
RunningAverage tracker = new FullRunningAverage();
evaluator.evaluate(recommender1, recommender2, 100, tracker, Formula.MEANRANK);
double eval = tracker.getAverage();
assertEquals(0.185294508934021, eval, EPSILON);
}