实例介绍
【实例简介】
【实例截图】
【实例截图】
【核心代码】
using org.apache.mahout.cf.taste.impl.model;
using org.apache.mahout.cf.taste.impl.model.file;
using org.apache.mahout.cf.taste.impl.neighborhood;
using org.apache.mahout.cf.taste.impl.recommender;
using org.apache.mahout.cf.taste.impl.recommender.knn;
using org.apache.mahout.cf.taste.impl.recommender.slopeone;
using org.apache.mahout.cf.taste.impl.similarity;
using org.apache.mahout.cf.taste.model;
using org.apache.mahout.cf.taste.similarity;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
namespace ntaste.Test
{
class Program
{
static void Main(string[] args)
{
SlopeOneRecommenderTest();
Console.WriteLine();
GenericUserBasedRecommenderTest();
Console.WriteLine();
KnnItemBasedRecommenderTest();
Console.WriteLine();
GenericItemBasedRecommenderTestByTanimotoCoefficientSimilarity();
Console.ReadLine();
}
static string filePath = @"E:\WorkStudio\ntaste\ntaste.Test\datafile\item.csv";
static void SlopeOneRecommenderTest()
{
Console.WriteLine("SlopeOneRecommenderTest");
var model = new FileDataModel(filePath);
var recommender = new SlopeOneRecommender(model);
var ids = model.getUserIDs();
while (ids.MoveNext())
{
var userId = ids.Current;
var recommendedItems = recommender.recommend(userId, 5);
Console.Write("uid:" userId);
foreach (var ritem in recommendedItems)
{
Console.Write("(" ritem.getItemID() "," ritem.getValue() ")");
}
Console.WriteLine();
}
}
static void GenericUserBasedRecommenderTest()
{
Console.WriteLine("GenericUserBasedRecommenderTest");
System.Diagnostics.Stopwatch watch = new System.Diagnostics.Stopwatch();
watch.Start();
var model = new FileDataModel(filePath);
var similarity = new PearsonCorrelationSimilarity(model);
var neighborhood = new NearestNUserNeighborhood(4, similarity, model);
var recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
var iter = model.getUserIDs();
while (iter.MoveNext())
{
var userId = iter.Current;
var recommendedItems = recommender.recommend(userId, 5);
Console.Write("uid:" userId);
foreach (var ritem in recommendedItems)
{
Console.Write("(" ritem.getItemID() "," ritem.getValue() ")");
}
Console.WriteLine();
}
watch.Stop();
Console.WriteLine(watch.ElapsedMilliseconds);
}
static void KnnItemBasedRecommenderTest()
{
Console.WriteLine("KnnItemBasedRecommenderTest");
System.Diagnostics.Stopwatch watch = new System.Diagnostics.Stopwatch();
watch.Start();
var model = new FileDataModel(filePath);
ItemSimilarity similarity = new LogLikelihoodSimilarity(model);
Optimizer optimizer = new ConjugateGradientOptimizer();
var recommender = new KnnItemBasedRecommender(model, similarity, optimizer, 10);
var iter = model.getUserIDs();
while (iter.MoveNext())
{
var userId = iter.Current;
var recommendedItems = recommender.recommend(userId, 5);
Console.Write("uid:" userId);
foreach (var ritem in recommendedItems)
{
Console.Write("(" ritem.getItemID() "," ritem.getValue() ")");
}
Console.WriteLine();
}
watch.Stop();
Console.WriteLine(watch.ElapsedMilliseconds);
}
static void GenericItemBasedRecommenderTestByTanimotoCoefficientSimilarity()
{
Console.WriteLine("GenericItemBasedRecommenderTestByTanimotoCoefficientSimilarity");
System.Diagnostics.Stopwatch watch = new System.Diagnostics.Stopwatch();
watch.Start();
var model = new FileDataModel(filePath);
ItemSimilarity similarity = new TanimotoCoefficientSimilarity(model);
var recommender = new GenericItemBasedRecommender(model, similarity);
var iter = model.getUserIDs();
while (iter.MoveNext())
{
var userId = iter.Current;
var recommendedItems = recommender.recommend(userId, 5);
Console.Write("uid:" userId);
foreach (var ritem in recommendedItems)
{
Console.Write("(" ritem.getItemID() "," ritem.getValue() ")");
}
Console.WriteLine();
}
watch.Stop();
Console.WriteLine(watch.ElapsedMilliseconds);
}
}
}
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