public double Estimate(long itemID)
{
IRunningAverage average = new FullRunningAverage();
double[] similarities = similarity.ItemSimilarities(itemID, toItemIDs);
for (int i = 0; i < toItemIDs.Length; i++)
{
long toItemID = toItemIDs[i];
Tuple <long, long> pair = new Tuple <long, long>(toItemID, itemID);
if (rescorer != null && rescorer.IsFiltered(pair))
{
continue;
}
double estimate = similarities[i];
if (rescorer != null)
{
estimate = rescorer.Rescore(pair, estimate);
}
if (excludeItemIfNotSimilarToAll || !Double.IsNaN(estimate))
{
average.AddDatum(estimate);
}
}
double averageEstimate = average.GetAverage();
return(averageEstimate == 0 ? Double.NaN : averageEstimate);
}
public double Estimate(long itemID)
{
Tuple <long, long> pair = new Tuple <long, long>(toItemID, itemID);
if (rescorer != null && rescorer.IsFiltered(pair))
{
return(Double.NaN);
}
double originalEstimate = similarity.ItemSimilarity(toItemID, itemID);
return(rescorer == null ? originalEstimate : rescorer.Rescore(pair, originalEstimate));
}
public double Estimate(long userID)
{
// Don't consider the user itself as a possible most similar user
if (userID == toUserID)
{
return(Double.NaN);
}
if (rescorer == null)
{
return(similarity.UserSimilarity(toUserID, userID));
}
else
{
Tuple <long, long> pair = new Tuple <long, long>(toUserID, userID);
if (rescorer.IsFiltered(pair))
{
return(Double.NaN);
}
double originalEstimate = similarity.UserSimilarity(toUserID, userID);
return(rescorer.Rescore(pair, originalEstimate));
}
}
