/// <summary>
/// Utility method to extract the recommended item from a xml recommendation result
/// </summary>
/// <param name="nodeList">the xml element containing the recommended items.</param>
/// <returns>a collection of recommended item or empty list id sh</returns>
internal static IEnumerable <RecommendedItem> ExtractRecommendedItems(XmlNodeList nodeList)
{
var recoList = new List <RecommendedItem>();
foreach (var node in (nodeList))
{
var item = new RecommendedItem();
//cycle through the recommended items
foreach (var child in ((XmlElement)node).ChildNodes)
{
//cycle through properties
var nodeName = ((XmlNode)child).LocalName;
switch (nodeName)
{
case "Id":
item.Id = ((XmlNode)child).InnerText;
break;
case "Name":
item.Name = ((XmlNode)child).InnerText;
break;
case "Rating":
item.Rating = ((XmlNode)child).InnerText;
break;
case "Reasoning":
item.Reasoning = ((XmlNode)child).InnerText;
break;
}
}
recoList.Add(item);
}
return(recoList);
}
public override bool Equals(object o)
{
if (!(o is GenericRecommendedItem))
{
return(false);
}
RecommendedItem item = (RecommendedItem)o;
return((this.itemID == item.getItemID()) && (this.value == item.getValue()));
}
public void TestRecommender()
{
Recommender recommender = buildRecommender();
IList <RecommendedItem> recommended = recommender.Recommend("test1", 1);
Assert.IsNotNull(recommended);
Assert.AreEqual(1, recommended.Count);
RecommendedItem firstRecommended = recommended[0];
Assert.AreEqual(new GenericItem <String>("2"), firstRecommended.Item);
Assert.AreEqual(0.3, firstRecommended.Value);
}
public void TestBestRating()
{
Recommender recommender = buildRecommender();
IList <RecommendedItem> recommended = recommender.Recommend("test1", 1);
Assert.IsNotNull(recommended);
Assert.AreEqual(1, recommended.Count);
RecommendedItem firstRecommended = recommended[0];
// item one should be recommended because it has a greater rating/score
Assert.AreEqual(new GenericItem <String>("2"), firstRecommended.Item);
Assert.AreEqual(0.3, firstRecommended.Value, EPSILON);
}
public void TestMostSimilar()
{
ItemBasedRecommender recommender = buildRecommender();
IList <RecommendedItem> similar = recommender.MostSimilarItems("0", 2);
Assert.IsNotNull(similar);
Assert.AreEqual(2, similar.Count);
RecommendedItem first = similar[0];
RecommendedItem second = similar[1];
Assert.AreEqual("1", first.Item.ID);
Assert.AreEqual(1.0, first.Value, EPSILON);
Assert.AreEqual("2", second.Item.ID);
Assert.AreEqual(0.5, second.Value, EPSILON);
}
public static int GetBestReasonItemId(this RecommendedItem item)
{
if (item.Reasons.Count == 0)
{
return(-1);
}
var bestReason = item.Reasons.OrderByDescending(it => it.Value).First();
var bestReasonItemId = 0;
if (Int32.TryParse(bestReason.Key, out bestReasonItemId))
{
return(bestReasonItemId);
}
return(-1);
}
/**
* Defines a natural ordering from most-preferred item (highest value) to least-preferred.
*
* @param other
* @return 1, -1, 0 as this value is less than, greater than or equal to the other's value
*/
public int CompareTo(RecommendedItem other)
{
double otherValue = other.Value;
if (value < otherValue)
{
return(1);
}
else if (value > otherValue)
{
return(-1);
}
else
{
return(0);
}
}
public void testRecommendedBecause()
{
ItemBasedRecommender recommender = buildRecommender2();
IList <RecommendedItem> recommendedBecause = recommender.RecommendedBecause("test1", "4", 3);
Assert.IsNotNull(recommendedBecause);
Assert.AreEqual(3, recommendedBecause.Count);
RecommendedItem first = recommendedBecause[0];
RecommendedItem second = recommendedBecause[1];
RecommendedItem third = recommendedBecause[2];
Assert.AreEqual("2", first.Item.ID);
Assert.AreEqual(0.99, first.Value, EPSILON);
Assert.AreEqual("3", second.Item.ID);
Assert.AreEqual(0.4, second.Value, EPSILON);
Assert.AreEqual("0", third.Item.ID);
Assert.AreEqual(0.2, third.Value, EPSILON);
}
public void TestBestRating()
{
List <User> users = new List <User>(3);
users.Add(GetUser("test1", 0.0, 0.3));
users.Add(GetUser("test2", 0.2, 0.3, 0.3));
users.Add(GetUser("test3", 0.4, 0.3, 0.5));
DataModel dataModel = new GenericDataModel(users);
Recommender recommender = new SlopeOneRecommender(dataModel);
IList <RecommendedItem> recommended = recommender.Recommend("test1", 1);
Assert.IsNotNull(recommended);
Assert.AreEqual(1, recommended.Count);
RecommendedItem firstRecommended = recommended[0];
// item one should be recommended because it has a greater rating/score
Assert.AreEqual(new GenericItem <String>("2"), firstRecommended.Item);
Assert.AreEqual(0.2400938676203033, firstRecommended.Value, EPSILON);
}
public void TestMostSimilarToMultiple()
{
ItemBasedRecommender recommender = buildRecommender2();
List <Object> itemIDs = new List <Object>(2);
itemIDs.Add("0");
itemIDs.Add("1");
IList <RecommendedItem> similar = recommender.MostSimilarItems(itemIDs, 2);
Assert.IsNotNull(similar);
Assert.AreEqual(2, similar.Count);
RecommendedItem first = similar[0];
RecommendedItem second = similar[1];
Assert.AreEqual("2", first.Item.ID);
Assert.AreEqual(0.85, first.Value, EPSILON);
Assert.AreEqual("3", second.Item.ID);
Assert.AreEqual(-0.3, second.Value, EPSILON);
}
public void TestBestRating()
{
List <User> users = new List <User>(4);
users.Add(GetUser("test1", 0.1, 0.3));
users.Add(GetUser("test2", 0.2, 0.3, 0.3));
users.Add(GetUser("test3", 0.4, 0.3, 0.5));
users.Add(GetUser("test4", 0.7, 0.3, 0.8));
DataModel dataModel = new GenericDataModel(users);
UserCorrelation correlation = new PearsonCorrelation(dataModel);
ClusterSimilarity similarity = new FarthestNeighborClusterSimilarity(correlation);
Recommender recommender = new TreeClusteringRecommender(dataModel, similarity, 2);
IList <RecommendedItem> recommended = recommender.Recommend("test1", 1);
Assert.IsNotNull(recommended);
Assert.AreEqual(1, recommended.Count);
RecommendedItem firstRecommended = recommended[0];
// item one should be recommended because it has a greater rating/score
Assert.AreEqual(new GenericItem <String>("2"), firstRecommended.Item);
Assert.AreEqual(0.3, firstRecommended.Value, EPSILON);
}
/// <summary>
/// Utility method to extract the recommended item from a xml recommendation result
/// </summary>
/// <param name="nodeList">the xml element containing the recommended items.</param>
/// <returns>a collection of recommended item or empty list id sh</returns>
internal static IEnumerable<RecommendedItem> ExtractRecommendedItems(XmlNodeList nodeList)
{
var recoList = new List<RecommendedItem>();
foreach (var node in (nodeList))
{
var item = new RecommendedItem();
//cycle through the recommended items
foreach (var child in ((XmlElement)node).ChildNodes)
{
//cycle through properties
var nodeName = ((XmlNode)child).LocalName;
switch (nodeName)
{
case "Id":
item.Id = ((XmlNode)child).InnerText;
break;
case "Name":
item.Name = ((XmlNode)child).InnerText;
break;
case "Rating":
item.Rating = ((XmlNode)child).InnerText;
break;
case "Reasoning":
item.Reasoning = ((XmlNode)child).InnerText;
break;
}
}
recoList.Add(item);
}
return recoList;
}
public IRStatistics evaluate(RecommenderBuilder recommenderBuilder, DataModelBuilder dataModelBuilder, DataModel dataModel, IDRescorer rescorer, int at, double relevanceThreshold, double evaluationPercentage)
{
int num = dataModel.getNumItems();
RunningAverage average = new FullRunningAverage();
RunningAverage average2 = new FullRunningAverage();
RunningAverage average3 = new FullRunningAverage();
RunningAverage average4 = new FullRunningAverage();
int num2 = 0;
int num3 = 0;
IEnumerator <long> enumerator = dataModel.getUserIDs();
while (enumerator.MoveNext())
{
long current = enumerator.Current;
if (this.random.nextDouble() < evaluationPercentage)
{
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
PreferenceArray prefs = dataModel.getPreferencesFromUser(current);
double num5 = double.IsNaN(relevanceThreshold) ? computeThreshold(prefs) : relevanceThreshold;
FastIDSet relevantItemIDs = this.dataSplitter.getRelevantItemsIDs(current, at, num5, dataModel);
int num6 = relevantItemIDs.size();
if (num6 > 0)
{
FastByIDMap <PreferenceArray> trainingUsers = new FastByIDMap <PreferenceArray>(dataModel.getNumUsers());
IEnumerator <long> enumerator2 = dataModel.getUserIDs();
while (enumerator2.MoveNext())
{
this.dataSplitter.processOtherUser(current, relevantItemIDs, trainingUsers, enumerator2.Current, dataModel);
}
DataModel model = (dataModelBuilder == null) ? new GenericDataModel(trainingUsers) : dataModelBuilder.buildDataModel(trainingUsers);
try
{
model.getPreferencesFromUser(current);
}
catch (NoSuchUserException)
{
continue;
}
int num7 = num6 + model.getItemIDsFromUser(current).size();
if (num7 >= (2 * at))
{
Recommender recommender = recommenderBuilder.buildRecommender(model);
int num8 = 0;
List <RecommendedItem> list = recommender.recommend(current, at, rescorer);
foreach (RecommendedItem item in list)
{
if (relevantItemIDs.contains(item.getItemID()))
{
num8++;
}
}
int count = list.Count;
if (count > 0)
{
average.addDatum(((double)num8) / ((double)count));
}
average2.addDatum(((double)num8) / ((double)num6));
if (num6 < num7)
{
average3.addDatum(((double)(count - num8)) / ((double)(num - num6)));
}
double num10 = 0.0;
double num11 = 0.0;
for (int i = 0; i < count; i++)
{
RecommendedItem item2 = list[i];
double num13 = 1.0 / log2(i + 2.0);
if (relevantItemIDs.contains(item2.getItemID()))
{
num10 += num13;
}
if (i < num6)
{
num11 += num13;
}
}
if (num11 > 0.0)
{
average4.addDatum(num10 / num11);
}
num2++;
if (count > 0)
{
num3++;
}
stopwatch.Stop();
log.info("Evaluated with user {} in {}ms", new object[] { current, stopwatch.ElapsedMilliseconds });
log.info("Precision/recall/fall-out/nDCG/reach: {} / {} / {} / {} / {}", new object[] { average.getAverage(), average2.getAverage(), average3.getAverage(), average4.getAverage(), ((double)num3) / ((double)num2) });
}
}
}
}
return(new IRStatisticsImpl(average.getAverage(), average2.getAverage(), average3.getAverage(), average4.getAverage(), ((double)num3) / ((double)num2)));
}
/**
* Defines a natural ordering from most-preferred item (highest value) to least-preferred.
*
* @param other
* @return 1, -1, 0 as this value is less than, greater than or equal to the other's value
*/
public int CompareTo(RecommendedItem other)
{
double otherValue = other.Value;
if (value < otherValue) {
return 1;
} else if (value > otherValue) {
return -1;
} else {
return 0;
}
}
