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)));
}
/// <summary>
/// Creates a new <see cref="GenericDataModel"/> from the given users (and their preferences). This
/// <see cref="IDataModel"/> retains all this information in memory and is effectively immutable.
/// </summary>
/// <param name="userData">users to include; (see also <see cref="GenericDataModel.ToDataMap(FastByIDMap, bool)"/>)</param>
/// <param name="timestamps">timestamps optionally, provided timestamps of preferences as milliseconds since the epoch. User IDs are mapped to maps of item IDs to long timestamps.</param>
public GenericDataModel(FastByIDMap <IPreferenceArray> userData, FastByIDMap <FastByIDMap <DateTime?> > timestamps)
{
//Preconditions.checkArgument(userData != null, "userData is null");
this.preferenceFromUsers = userData;
FastByIDMap <IList <IPreference> > prefsForItems = new FastByIDMap <IList <IPreference> >();
FastIDSet itemIDSet = new FastIDSet();
int currentCount = 0;
float maxPrefValue = float.NegativeInfinity;
float minPrefValue = float.PositiveInfinity;
foreach (var entry in preferenceFromUsers.EntrySet())
{
IPreferenceArray prefs = entry.Value;
prefs.SortByItem();
foreach (IPreference preference in prefs)
{
long itemID = preference.GetItemID();
itemIDSet.Add(itemID);
var prefsForItem = prefsForItems.Get(itemID);
if (prefsForItem == null)
{
prefsForItem = new List <IPreference>(2);
prefsForItems.Put(itemID, prefsForItem);
}
prefsForItem.Add(preference);
float value = preference.GetValue();
if (value > maxPrefValue)
{
maxPrefValue = value;
}
if (value < minPrefValue)
{
minPrefValue = value;
}
}
if (++currentCount % 10000 == 0)
{
log.Info("Processed {0} users", currentCount);
}
}
log.Info("Processed {0} users", currentCount);
setMinPreference(minPrefValue);
setMaxPreference(maxPrefValue);
this.itemIDs = itemIDSet.ToArray();
itemIDSet = null; // Might help GC -- this is big
Array.Sort(itemIDs);
this.preferenceForItems = ToDataMap(prefsForItems, false);
foreach (var entry in preferenceForItems.EntrySet())
{
entry.Value.SortByUser();
}
this.userIDs = new long[userData.Count()];
int i = 0;
foreach (var v in userData.Keys)
{
userIDs[i++] = v;
}
Array.Sort(userIDs);
this.timestamps = timestamps;
}
