public List <RecommendedItem> recommendedBecause(long userID, long itemID, int howMany)
{
DataModel model = this.getDataModel();
TopItems.Estimator <long> estimator = new RecommendedBecauseEstimator(this, userID, itemID);
PreferenceArray array = model.getPreferencesFromUser(userID);
int size = array.length();
FastIDSet set = new FastIDSet(size);
for (int i = 0; i < size; i++)
{
set.add(array.getItemID(i));
}
set.remove(itemID);
return(TopItems.getTopItems(howMany, set.GetEnumerator(), null, estimator));
}
private static int mask(FastIDSet commonSet, FastIDSet otherSet, long maxItemID)
{
int num = 0;
for (int i = 0; i <= maxItemID; i++)
{
if (commonSet.contains((long)i))
{
if (otherSet.contains((long)i))
{
num++;
}
else
{
commonSet.remove((long)i);
}
}
}
return(num);
}
protected override float doEstimatePreference(long theUserID, PreferenceArray preferencesFromUser, long itemID)
{
DataModel dataModel = getDataModel();
int size = preferencesFromUser.length();
FastIDSet possibleItemIDs = new FastIDSet(size);
for (int i = 0; i < size; i++)
{
possibleItemIDs.add(preferencesFromUser.getItemID(i));
}
possibleItemIDs.remove(itemID);
List <RecommendedItem> mostSimilar = mostSimilarItems(itemID, possibleItemIDs.GetEnumerator(), neighborhoodSize, null);
long[] theNeighborhood = new long[mostSimilar.Count() + 1];
theNeighborhood[0] = -1;
List <long> usersRatedNeighborhood = new List <long>();
int nOffset = 0;
foreach (RecommendedItem rec in mostSimilar)
{
theNeighborhood[nOffset++] = rec.getItemID();
}
if (mostSimilar.Count != 0)
{
theNeighborhood[mostSimilar.Count] = itemID;
for (int i = 0; i < theNeighborhood.Length; i++)
{
PreferenceArray usersNeighborhood = dataModel.getPreferencesForItem(theNeighborhood[i]);
int size1 = usersRatedNeighborhood.Count == 0 ? usersNeighborhood.length() : usersRatedNeighborhood.Count;
for (int j = 0; j < size1; j++)
{
if (i == 0)
{
usersRatedNeighborhood.Add(usersNeighborhood.getUserID(j));
}
else
{
if (j >= usersRatedNeighborhood.Count)
{
break;
}
long index = usersRatedNeighborhood[j];
if (!usersNeighborhood.hasPrefWithUserID(index) || index == theUserID)
{
usersRatedNeighborhood.Remove(index);
j--;
}
}
}
}
}
double[] weights = null;
if (mostSimilar.Count != 0)
{
weights = getInterpolations(itemID, theNeighborhood, usersRatedNeighborhood);
}
int n = 0;
double preference = 0.0;
double totalSimilarity = 0.0;
foreach (long jitem in theNeighborhood)
{
float?pref = dataModel.getPreferenceValue(theUserID, jitem);
if (pref != null)
{
double weight = weights[n];
preference += pref.Value * weight;
totalSimilarity += weight;
}
n++;
}
return(totalSimilarity == 0.0 ? float.NaN : (float)(preference / totalSimilarity));
}
