public double UserSimilarity(long userID1, long userID2)
{
IDataModel dataModel = getDataModel();
FastIDSet xPrefs = dataModel.GetItemIDsFromUser(userID1);
FastIDSet yPrefs = dataModel.GetItemIDsFromUser(userID2);
int xPrefsSize = xPrefs.Count();
int yPrefsSize = yPrefs.Count();
if (xPrefsSize == 0 && yPrefsSize == 0)
{
return(Double.NaN);
}
if (xPrefsSize == 0 || yPrefsSize == 0)
{
return(0.0);
}
int intersectionSize =
xPrefsSize < yPrefsSize?yPrefs.IntersectionSize(xPrefs) : xPrefs.IntersectionSize(yPrefs);
if (intersectionSize == 0)
{
return(Double.NaN);
}
int unionSize = xPrefsSize + yPrefsSize - intersectionSize;
return((double)intersectionSize / (double)unionSize);
}
public double UserSimilarity(long userID1, long userID2)
{
IDataModel dataModel = getDataModel();
FastIDSet prefs1 = dataModel.GetItemIDsFromUser(userID1);
FastIDSet prefs2 = dataModel.GetItemIDsFromUser(userID2);
int prefs1Size = prefs1.Count();
int prefs2Size = prefs2.Count();
int intersectionSize = prefs1Size < prefs2Size?prefs2.IntersectionSize(prefs1) : prefs1.IntersectionSize(prefs2);
return(doSimilarity(prefs1Size, prefs2Size, intersectionSize));
}
protected FastIDSet getAllOtherItems(long[] theNeighborhood, long theUserID)
{
IDataModel dataModel = GetDataModel();
FastIDSet possibleItemIDs = new FastIDSet();
foreach (long userID in theNeighborhood)
{
possibleItemIDs.AddAll(dataModel.GetItemIDsFromUser(userID));
}
possibleItemIDs.RemoveAll(dataModel.GetItemIDsFromUser(theUserID));
return(possibleItemIDs);
}
protected override FastIDSet doGetCandidateItems(long[] preferredItemIDs, IDataModel dataModel) {
FastIDSet possibleItemsIDs = new FastIDSet();
foreach (long itemID in preferredItemIDs) {
IPreferenceArray itemPreferences = dataModel.GetPreferencesForItem(itemID);
int numUsersPreferringItem = itemPreferences.Length();
for (int index = 0; index < numUsersPreferringItem; index++) {
possibleItemsIDs.AddAll(dataModel.GetItemIDsFromUser(itemPreferences.GetUserID(index)));
}
}
possibleItemsIDs.RemoveAll(preferredItemIDs);
return possibleItemsIDs;
}
/// Exports the simple user IDs and associated item IDs in the data model.
///
/// @return a {@link FastByIDMap} mapping user IDs to {@link FastIDSet}s representing
/// that user's associated items
public static FastByIDMap <FastIDSet> toDataMap(IDataModel dataModel)
{
FastByIDMap <FastIDSet> data = new FastByIDMap <FastIDSet>(dataModel.GetNumUsers());
var it = dataModel.GetUserIDs();
while (it.MoveNext())
{
long userID = it.Current;
data.Put(userID, dataModel.GetItemIDsFromUser(userID));
}
return(data);
}
public virtual FastIDSet GetItemIDsFromUser(long userID)
{
if (userID == TEMP_USER_ID)
{
if (tempPrefs == null)
{
throw new NoSuchUserException(TEMP_USER_ID);
}
return(prefItemIDs);
}
return(_delegate.GetItemIDsFromUser(userID));
}
protected override FastIDSet doGetCandidateItems(long[] preferredItemIDs, IDataModel dataModel)
{
var preferredItemIDsIterator = ((IEnumerable <long>)preferredItemIDs).GetEnumerator();
if (preferredItemIDs.Length > maxItems)
{
double samplingRate = (double)maxItems / preferredItemIDs.Length;
log.Info("preferredItemIDs.Length {0}, samplingRate {1}", preferredItemIDs.Length, samplingRate);
preferredItemIDsIterator =
new SamplinglongPrimitiveIterator(preferredItemIDsIterator, samplingRate);
}
FastIDSet possibleItemsIDs = new FastIDSet();
while (preferredItemIDsIterator.MoveNext())
{
long itemID = preferredItemIDsIterator.Current;
IPreferenceArray prefs = dataModel.GetPreferencesForItem(itemID);
int prefsLength = prefs.Length();
if (prefsLength > maxUsersPerItem)
{
var sampledPrefs =
new FixedSizeSamplingIterator <IPreference>(maxUsersPerItem, prefs.GetEnumerator());
while (sampledPrefs.MoveNext())
{
addSomeOf(possibleItemsIDs, dataModel.GetItemIDsFromUser(sampledPrefs.Current.GetUserID()));
}
}
else
{
for (int i = 0; i < prefsLength; i++)
{
addSomeOf(possibleItemsIDs, dataModel.GetItemIDsFromUser(prefs.GetUserID(i)));
}
}
}
possibleItemsIDs.RemoveAll(preferredItemIDs);
return(possibleItemsIDs);
}
public double UserSimilarity(long userID1, long userID2)
{
IDataModel dataModel = getDataModel();
FastIDSet prefs1 = dataModel.GetItemIDsFromUser(userID1);
FastIDSet prefs2 = dataModel.GetItemIDsFromUser(userID2);
long prefs1Size = prefs1.Count();
long prefs2Size = prefs2.Count();
long intersectionSize =
prefs1Size < prefs2Size?prefs2.IntersectionSize(prefs1) : prefs1.IntersectionSize(prefs2);
if (intersectionSize == 0)
{
return(Double.NaN);
}
long numItems = dataModel.GetNumItems();
double logLikelihood =
LogLikelihood.logLikelihoodRatio(intersectionSize,
prefs2Size - intersectionSize,
prefs1Size - intersectionSize,
numItems - prefs1Size - prefs2Size + intersectionSize);
return(1.0 - 1.0 / (1.0 + logLikelihood));
}
protected override FastIDSet doGetCandidateItems(long[] preferredItemIDs, IDataModel dataModel)
{
FastIDSet possibleItemsIDs = new FastIDSet();
foreach (long itemID in preferredItemIDs)
{
IPreferenceArray itemPreferences = dataModel.GetPreferencesForItem(itemID);
int numUsersPreferringItem = itemPreferences.Length();
for (int index = 0; index < numUsersPreferringItem; index++)
{
possibleItemsIDs.AddAll(dataModel.GetItemIDsFromUser(itemPreferences.GetUserID(index)));
}
}
possibleItemsIDs.RemoveAll(preferredItemIDs);
return(possibleItemsIDs);
}
protected override FastIDSet doGetCandidateItems(long[] preferredItemIDs, IDataModel dataModel) {
var preferredItemIDsIterator = ((IEnumerable<long>)preferredItemIDs).GetEnumerator();
if (preferredItemIDs.Length > maxItems) {
double samplingRate = (double) maxItems / preferredItemIDs.Length;
log.Info("preferredItemIDs.Length {0}, samplingRate {1}", preferredItemIDs.Length, samplingRate);
preferredItemIDsIterator =
new SamplinglongPrimitiveIterator(preferredItemIDsIterator, samplingRate);
}
FastIDSet possibleItemsIDs = new FastIDSet();
while (preferredItemIDsIterator.MoveNext()) {
long itemID = preferredItemIDsIterator.Current;
IPreferenceArray prefs = dataModel.GetPreferencesForItem(itemID);
int prefsLength = prefs.Length();
if (prefsLength > maxUsersPerItem) {
var sampledPrefs =
new FixedSizeSamplingIterator<IPreference>(maxUsersPerItem, prefs.GetEnumerator());
while (sampledPrefs.MoveNext()) {
addSomeOf(possibleItemsIDs, dataModel.GetItemIDsFromUser(sampledPrefs.Current.GetUserID()));
}
} else {
for (int i = 0; i < prefsLength; i++) {
addSomeOf(possibleItemsIDs, dataModel.GetItemIDsFromUser(prefs.GetUserID(i)));
}
}
}
possibleItemsIDs.RemoveAll(preferredItemIDs);
return possibleItemsIDs;
}
/// Exports the simple user IDs and associated item IDs in the data model.
///
/// @return a {@link FastByIDMap} mapping user IDs to {@link FastIDSet}s representing
/// that user's associated items
public static FastByIDMap<FastIDSet> toDataMap(IDataModel dataModel) {
FastByIDMap<FastIDSet> data = new FastByIDMap<FastIDSet>(dataModel.GetNumUsers());
var it = dataModel.GetUserIDs();
while (it.MoveNext()) {
long userID = it.Current;
data.Put(userID, dataModel.GetItemIDsFromUser(userID));
}
return data;
}
public override Factorization Factorize()
{
log.Info("starting to compute the factorization...");
Features features = new Features(this);
/// feature maps necessary for solving for implicit feedback
IDictionary <int, double[]> userY = null;
IDictionary <int, double[]> itemY = null;
if (usesImplicitFeedback)
{
userY = userFeaturesMapping(dataModel.GetUserIDs(), dataModel.GetNumUsers(), features.getU());
itemY = itemFeaturesMapping(dataModel.GetItemIDs(), dataModel.GetNumItems(), features.getM());
}
IList <Task> tasks;
for (int iteration = 0; iteration < numIterations; iteration++)
{
log.Info("iteration {0}", iteration);
/// fix M - compute U
tasks = new List <Task>();
var userIDsIterator = dataModel.GetUserIDs();
try {
ImplicitFeedbackAlternatingLeastSquaresSolver implicitFeedbackSolver = usesImplicitFeedback
? new ImplicitFeedbackAlternatingLeastSquaresSolver(numFeatures, lambda, alpha, itemY) : null;
while (userIDsIterator.MoveNext())
{
long userID = userIDsIterator.Current;
var itemIDsFromUser = dataModel.GetItemIDsFromUser(userID).GetEnumerator();
IPreferenceArray userPrefs = dataModel.GetPreferencesFromUser(userID);
tasks.Add(Task.Factory.StartNew(() => {
List <double[]> featureVectors = new List <double[]>();
while (itemIDsFromUser.MoveNext())
{
long itemID = itemIDsFromUser.Current;
featureVectors.Add(features.getItemFeatureColumn(itemIndex(itemID)));
}
var userFeatures = usesImplicitFeedback
? implicitFeedbackSolver.solve(sparseUserRatingVector(userPrefs))
: AlternatingLeastSquaresSolver.solve(featureVectors, ratingVector(userPrefs), lambda, numFeatures);
features.setFeatureColumnInU(userIndex(userID), userFeatures);
}
));
}
} finally {
// queue.shutdown();
try {
Task.WaitAll(tasks.ToArray(), 1000 * dataModel.GetNumUsers());
} catch (AggregateException e) {
log.Warn("Error when computing user features", e);
throw e;
}
}
/// fix U - compute M
//queue = createQueue();
tasks = new List <Task>();
var itemIDsIterator = dataModel.GetItemIDs();
try {
ImplicitFeedbackAlternatingLeastSquaresSolver implicitFeedbackSolver = usesImplicitFeedback
? new ImplicitFeedbackAlternatingLeastSquaresSolver(numFeatures, lambda, alpha, userY) : null;
while (itemIDsIterator.MoveNext())
{
long itemID = itemIDsIterator.Current;
IPreferenceArray itemPrefs = dataModel.GetPreferencesForItem(itemID);
tasks.Add(Task.Factory.StartNew(() => {
var featureVectors = new List <double[]>();
foreach (IPreference pref in itemPrefs)
{
long userID = pref.GetUserID();
featureVectors.Add(features.getUserFeatureColumn(userIndex(userID)));
}
var itemFeatures = usesImplicitFeedback
? implicitFeedbackSolver.solve(sparseItemRatingVector(itemPrefs))
: AlternatingLeastSquaresSolver.solve(featureVectors, ratingVector(itemPrefs), lambda, numFeatures);
features.setFeatureColumnInM(itemIndex(itemID), itemFeatures);
}));
}
} finally {
try {
Task.WaitAll(tasks.ToArray(), 1000 * dataModel.GetNumItems());
//queue.awaitTermination(dataModel.getNumItems(), TimeUnit.SECONDS);
} catch (AggregateException e) {
log.Warn("Error when computing item features", e);
throw e;
}
}
}
log.Info("finished computation of the factorization...");
return(createFactorization(features.getU(), features.getM()));
}
public IRStatistics Evaluate(IRecommenderBuilder recommenderBuilder,
IDataModelBuilder dataModelBuilder,
IDataModel dataModel,
IDRescorer rescorer,
int at,
double relevanceThreshold,
double evaluationPercentage)
{
//Preconditions.checkArgument(recommenderBuilder != null, "recommenderBuilder is null");
//Preconditions.checkArgument(dataModel != null, "dataModel is null");
//Preconditions.checkArgument(at >= 1, "at must be at least 1");
//Preconditions.checkArgument(evaluationPercentage > 0.0 && evaluationPercentage <= 1.0,
// "Invalid evaluationPercentage: " + evaluationPercentage + ". Must be: 0.0 < evaluationPercentage <= 1.0");
int numItems = dataModel.GetNumItems();
IRunningAverage precision = new FullRunningAverage();
IRunningAverage recall = new FullRunningAverage();
IRunningAverage fallOut = new FullRunningAverage();
IRunningAverage nDCG = new FullRunningAverage();
int numUsersRecommendedFor = 0;
int numUsersWithRecommendations = 0;
var it = dataModel.GetUserIDs();
while (it.MoveNext())
{
long userID = it.Current;
if (random.nextDouble() >= evaluationPercentage)
{
// Skipped
continue;
}
var stopWatch = new System.Diagnostics.Stopwatch();
stopWatch.Start();
IPreferenceArray prefs = dataModel.GetPreferencesFromUser(userID);
// List some most-preferred items that would count as (most) "relevant" results
double theRelevanceThreshold = Double.IsNaN(relevanceThreshold) ? computeThreshold(prefs) : relevanceThreshold;
FastIDSet relevantItemIDs = dataSplitter.GetRelevantItemsIDs(userID, at, theRelevanceThreshold, dataModel);
int numRelevantItems = relevantItemIDs.Count();
if (numRelevantItems <= 0)
{
continue;
}
FastByIDMap <IPreferenceArray> trainingUsers = new FastByIDMap <IPreferenceArray>(dataModel.GetNumUsers());
var it2 = dataModel.GetUserIDs();
while (it2.MoveNext())
{
dataSplitter.ProcessOtherUser(userID, relevantItemIDs, trainingUsers, it2.Current, dataModel);
}
IDataModel trainingModel = dataModelBuilder == null ? new GenericDataModel(trainingUsers)
: dataModelBuilder.BuildDataModel(trainingUsers);
try {
trainingModel.GetPreferencesFromUser(userID);
} catch (NoSuchUserException nsee) {
continue; // Oops we excluded all prefs for the user -- just move on
}
int size = numRelevantItems + trainingModel.GetItemIDsFromUser(userID).Count();
if (size < 2 * at)
{
// Really not enough prefs to meaningfully evaluate this user
continue;
}
IRecommender recommender = recommenderBuilder.BuildRecommender(trainingModel);
int intersectionSize = 0;
var recommendedItems = recommender.Recommend(userID, at, rescorer);
foreach (IRecommendedItem recommendedItem in recommendedItems)
{
if (relevantItemIDs.Contains(recommendedItem.GetItemID()))
{
intersectionSize++;
}
}
int numRecommendedItems = recommendedItems.Count;
// Precision
if (numRecommendedItems > 0)
{
precision.AddDatum((double)intersectionSize / (double)numRecommendedItems);
}
// Recall
recall.AddDatum((double)intersectionSize / (double)numRelevantItems);
// Fall-out
if (numRelevantItems < size)
{
fallOut.AddDatum((double)(numRecommendedItems - intersectionSize)
/ (double)(numItems - numRelevantItems));
}
// nDCG
// In computing, assume relevant IDs have relevance 1 and others 0
double cumulativeGain = 0.0;
double idealizedGain = 0.0;
for (int i = 0; i < numRecommendedItems; i++)
{
IRecommendedItem item = recommendedItems[i];
double discount = 1.0 / log2(i + 2.0); // Classical formulation says log(i+1), but i is 0-based here
if (relevantItemIDs.Contains(item.GetItemID()))
{
cumulativeGain += discount;
}
// otherwise we're multiplying discount by relevance 0 so it doesn't do anything
// Ideally results would be ordered with all relevant ones first, so this theoretical
// ideal list starts with number of relevant items equal to the total number of relevant items
if (i < numRelevantItems)
{
idealizedGain += discount;
}
}
if (idealizedGain > 0.0)
{
nDCG.AddDatum(cumulativeGain / idealizedGain);
}
// Reach
numUsersRecommendedFor++;
if (numRecommendedItems > 0)
{
numUsersWithRecommendations++;
}
stopWatch.Stop();
log.Info("Evaluated with user {} in {}ms", userID, stopWatch.ElapsedMilliseconds);
log.Info("Precision/recall/fall-out/nDCG/reach: {} / {} / {} / {} / {}",
precision.GetAverage(), recall.GetAverage(), fallOut.GetAverage(), nDCG.GetAverage(),
(double)numUsersWithRecommendations / (double)numUsersRecommendedFor);
}
return(new IRStatisticsImpl(
precision.GetAverage(),
recall.GetAverage(),
fallOut.GetAverage(),
nDCG.GetAverage(),
(double)numUsersWithRecommendations / (double)numUsersRecommendedFor));
}
public override FastIDSet GetItemIDsFromUser(long userID)
{
return(_delegate.GetItemIDsFromUser(userID));
}
