protected virtual float doEstimatePreference(long userID, IPreferenceArray preferencesFromUser, long itemID)
{
double preference = 0.0;
double totalSimilarity = 0.0;
int count = 0;
double[] similarities = similarity.ItemSimilarities(itemID, preferencesFromUser.GetIDs());
for (int i = 0; i < similarities.Length; i++)
{
double theSimilarity = similarities[i];
if (!Double.IsNaN(theSimilarity))
{
// Weights can be negative!
preference += theSimilarity * preferencesFromUser.GetValue(i);
totalSimilarity += theSimilarity;
count++;
}
}
// Throw out the estimate if it was based on no data points, of course, but also if based on
// just one. This is a bit of a band-aid on the 'stock' item-based algorithm for the moment.
// The reason is that in this case the estimate is, simply, the user's rating for one item
// that happened to have a defined similarity. The similarity score doesn't matter, and that
// seems like a bad situation.
if (count <= 1)
{
return(float.NaN);
}
float estimate = (float)(preference / totalSimilarity);
if (capper != null)
{
estimate = capper.capEstimate(estimate);
}
return(estimate);
}
/// This computation is in a technical sense, wrong, since in the domain of "bool preference users" where
/// all preference values are 1, this method should only ever return 1.0 or NaN. This isn't terribly useful
/// however since it means results can't be ranked by preference value (all are 1). So instead this returns a
/// sum of similarities.
protected override float doEstimatePreference(long userID, IPreferenceArray preferencesFromUser, long itemID)
{
double[] similarities = getSimilarity().ItemSimilarities(itemID, preferencesFromUser.GetIDs());
bool foundAPref = false;
double totalSimilarity = 0.0;
foreach (double theSimilarity in similarities) {
if (!Double.IsNaN(theSimilarity)) {
foundAPref = true;
totalSimilarity += theSimilarity;
}
}
return foundAPref ? (float) totalSimilarity : float.NaN;
}
/// This computation is in a technical sense, wrong, since in the domain of "bool preference users" where
/// all preference values are 1, this method should only ever return 1.0 or NaN. This isn't terribly useful
/// however since it means results can't be ranked by preference value (all are 1). So instead this returns a
/// sum of similarities.
protected override float doEstimatePreference(long userID, IPreferenceArray preferencesFromUser, long itemID)
{
double[] similarities = getSimilarity().ItemSimilarities(itemID, preferencesFromUser.GetIDs());
bool foundAPref = false;
double totalSimilarity = 0.0;
foreach (double theSimilarity in similarities)
{
if (!Double.IsNaN(theSimilarity))
{
foundAPref = true;
totalSimilarity += theSimilarity;
}
}
return(foundAPref ? (float)totalSimilarity : float.NaN);
}
public FastIDSet GetCandidateItems(long userID, IPreferenceArray preferencesFromUser, IDataModel dataModel)
{
return(doGetCandidateItems(preferencesFromUser.GetIDs(), dataModel));
}
public FastIDSet GetCandidateItems(long userID, IPreferenceArray preferencesFromUser, IDataModel dataModel)
{
return doGetCandidateItems(preferencesFromUser.GetIDs(), dataModel);
}
