This question already has an answer here:
I am trying to make a simple recommender system, and I found that with mahout it is pretty easy to make one. I have the following code (I am running it on eclipse and everything works great:
package com.predictionmarketing.RecommenderApp;
import java.io.File;
import java.io.IOException;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
import org.apache.mahout.cf.taste.impl.neighborhood.ThresholdUserNeighborhood;
import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.UserBasedRecommender;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
/**
* Java's application, user based recommender system
*
*/
public class App
{
public static void main( String[] args )
{
// Modelo
DataModel model = null;
// Inicializar similaridad
UserSimilarity similarity = null;
// Leer .cv userID, itemID, value
try {
model = new FileDataModel(new File("data/dataset.csv"));
} catch (IOException e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
// Encontrar matriz de similaridad
try {
similarity = new PearsonCorrelationSimilarity(model);
} catch (TasteException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
java.util.List<RecommendedItem> recommendations = null;
try {
recommendations = recommender.recommend(2, 3);
} catch (TasteException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
// Mostrar Recomendaciones
for (RecommendedItem recommendation : recommendations) {
System.out.println(recommendation.getItemID());
}
}
}
However, I need to run this code online because I am making the application on PHP and that is where my problem arises. Is there a way to run this code on PHP, so I can use the "recommendation" variable?
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