private List <RecommendedItem> mostSimilarItems(long itemID,
IEnumerator <long> possibleItemIDs,
int howMany,
Rescorer <Tuple <long, long> > rescorer)
{
TopItems.Estimator <long> estimator = new MostSimilarEstimator(itemID, getSimilarity(), rescorer);
return(TopItems.getTopItems(howMany, possibleItemIDs, null, estimator));
}
public override List <RecommendedItem> recommend(long userID, int howMany, IDRescorer rescorer)
{
log.debug("Recommending items for user ID '{}'", new object[] { userID });
PreferenceArray preferencesFromUser = this.getDataModel().getPreferencesFromUser(userID);
List <RecommendedItem> list = TopItems.getTopItems(howMany, this.getAllOtherItems(userID, preferencesFromUser).GetEnumerator(), rescorer, new Estimator(this, userID));
log.debug("Recommendations are: {}", new object[] { list });
return(list);
}
public override List <RecommendedItem> recommend(long userID, int howMany, taste.recommender.IDRescorer rescorer)
{
//Preconditions.checkArgument(howMany >= 1, "howMany must be at least 1");
log.debug("Recommending items for user ID '{}'", userID);
FastIDSet possibleItemIDs = diffStorage.getRecommendableItemIDs(userID);
TopItems.Estimator <long> estimator = new Estimator(this, userID);
List <RecommendedItem> topItems = TopItems.getTopItems(howMany, possibleItemIDs.GetEnumerator(), rescorer, estimator);
log.debug("Recommendations are: {}", topItems);
return(topItems);
}
private List <RecommendedItem> computeTopRecsForCluster(FastIDSet cluster)
{
DataModel dataModel = getDataModel();
FastIDSet possibleItemIDs = new FastIDSet();
var it = cluster.GetEnumerator();
while (it.MoveNext())
{
possibleItemIDs.addAll(dataModel.getItemIDsFromUser(it.Current));
}
TopItems.Estimator <long> estimator = new Estimator(cluster, this);
List <RecommendedItem> topItems = TopItems.getTopItems(NUM_CLUSTER_RECS,
possibleItemIDs.GetEnumerator(), null, estimator);
log.debug("Recommendations are: {}", topItems);
return(topItems);
}