public double UserSimilarity(long userID1, long userID2)
{
IPreferenceArray xPrefs = dataModel.GetPreferencesFromUser(userID1);
IPreferenceArray yPrefs = dataModel.GetPreferencesFromUser(userID2);
int xLength = xPrefs.Length();
int yLength = yPrefs.Length();
if (xLength <= 1 || yLength <= 1)
{
return(Double.NaN);
}
// Copy prefs since we need to modify pref values to ranks
xPrefs = xPrefs.Clone();
yPrefs = yPrefs.Clone();
// First sort by values from low to high
xPrefs.SortByValue();
yPrefs.SortByValue();
// Assign ranks from low to high
float nextRank = 1.0f;
for (int i = 0; i < xLength; i++)
{
// ... but only for items that are common to both pref arrays
if (yPrefs.HasPrefWithItemID(xPrefs.GetItemID(i)))
{
xPrefs.SetValue(i, nextRank);
nextRank += 1.0f;
}
// Other values are bogus but don't matter
}
nextRank = 1.0f;
for (int i = 0; i < yLength; i++)
{
if (xPrefs.HasPrefWithItemID(yPrefs.GetItemID(i)))
{
yPrefs.SetValue(i, nextRank);
nextRank += 1.0f;
}
}
xPrefs.SortByItem();
yPrefs.SortByItem();
long xIndex = xPrefs.GetItemID(0);
long yIndex = yPrefs.GetItemID(0);
int xPrefIndex = 0;
int yPrefIndex = 0;
double sumXYRankDiff2 = 0.0;
int count = 0;
while (true)
{
int compare = xIndex <yIndex ? -1 : xIndex> yIndex ? 1 : 0;
if (compare == 0)
{
double diff = xPrefs.GetValue(xPrefIndex) - yPrefs.GetValue(yPrefIndex);
sumXYRankDiff2 += diff * diff;
count++;
}
if (compare <= 0)
{
if (++xPrefIndex >= xLength)
{
break;
}
xIndex = xPrefs.GetItemID(xPrefIndex);
}
if (compare >= 0)
{
if (++yPrefIndex >= yLength)
{
break;
}
yIndex = yPrefs.GetItemID(yPrefIndex);
}
}
if (count <= 1)
{
return(Double.NaN);
}
// When ranks are unique, this formula actually gives the Pearson correlation
return(1.0 - 6.0 * sumXYRankDiff2 / (count * (count * count - 1)));
}
