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+package nearsoft.academy.bigdata.recommendation;
+
+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;
+//Librerías de mahoot necesarias para el recommender
+
+import java.io.*;
+import java.util.*;
+import java.util.zip.*;
+//Librerías para el manejo de archivos zip en java
+
+public class MovieRecommender {
+
+    HashMap<String, Integer> uniqueUser = new HashMap<String, Integer>();
+    HashMap<String, Integer> uniqueProduct = new HashMap<String, Integer>();
+    int totalReviews = 0;
+    //Aquí declaramos las variables globales que usaremos y devolveremos en métodos
+
+    public MovieRecommender(String filename) throws IOException{
+        //Constructor de la clase
+
+        InputStream inputStream = new GZIPInputStream(new FileInputStream(filename));
+        BufferedReader br = new BufferedReader(new InputStreamReader(inputStream, "UTF-8"));
+        String strCurrentLine;
+        //Construimos un objeto de BufferedReader para el archivo gzip dado
+
+        String prodPattern = "product/productId: ";
+        String userPattern = "review/userId: ";
+        String scorePattern = "review/score: ";
+        //Variables con el patrón que buscaremos en las líneas
+
+        String product = "";
+        int productCont = 0;
+        String user = "";
+        int userCont = 0;
+        String score = "";
+        //Variables auxiliares donde almacenamos los valores de cada línea
+
+        File moviesData = new File("data/moviesdb.csv");
+        FileWriter writer = new FileWriter(moviesData);
+        BufferedWriter lineWritter = new BufferedWriter(writer);
+        //Construimos objeto para la creación y escritura de archivo csv
+
+            while((strCurrentLine = br.readLine()) != null){
+            //Cíclo para recorrer línea a línea del archivo
+
+                if(strCurrentLine.contains(prodPattern)) {
+                    product = strCurrentLine.substring(prodPattern.length());
+                    if(!this.uniqueProduct.containsKey(product)){
+                        this.uniqueProduct.put(product, productCont);
+                        productCont++;
+                    }
+
+                }
+                if(strCurrentLine.contains(userPattern)) {
+                    user = strCurrentLine.substring(userPattern.length());
+                    if(!this.uniqueUser.containsKey(user)){
+                        this.uniqueUser.put(user, userCont);
+                        userCont++;
+                    }
+                }
+                if(strCurrentLine.contains(scorePattern)) {
+                    this.totalReviews++;
+                    score = strCurrentLine.substring(scorePattern.length());
+                    lineWritter.write(this.uniqueUser.get(user) + "," + this.uniqueProduct.get(product) + "," + score + "\n");
+                }
+                //Condicionales para encontrar patrones en las líneas,
+                //si se encuentra cierto patrón se procesa la línea dependiendo
+                //el patrón que se encuentr
+        }
+            lineWritter.close();
+            writer.close();
+            br.close();
+            inputStream.close();
+
+
+    }
+
+    public String getKey(int val)
+    {
+        for(String key : this.uniqueProduct.keySet()){
+            if(this.uniqueProduct.get(key) == val){
+                return key;
+            }
+        }
+        return null;
+    }
+    //Método para devolver el key del hashmap de productos dado un valor
+
+    public List<String> getRecommendationsForUser(String user) throws IOException, TasteException {
+
+        int numUser = this.uniqueUser.get(user);
+        List<String> recs = new ArrayList<String>();
+
+        DataModel model = new FileDataModel(new File("data/moviesdb.csv"));
+        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
+        UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
+        UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
+        List<RecommendedItem> recommendations = recommender.recommend(numUser, 3);
+        for (RecommendedItem recommendation : recommendations){
+            recs.add(getKey((int)recommendation.getItemID()));
+        }
+        return recs;
+    }
+    //Implementación del recommender de 5 min dada la base de datos que constuimos
+
+    public int getTotalReviews(){
+        return this.totalReviews;
+    }
+    //Método para devolver el total de reviews
+
+    public int getTotalProducts(){
+        return this.uniqueProduct.size();
+    }
+    //Método para devolver el total de productos únicos
+
+    public int getTotalUsers(){
+        return this.uniqueUser.size();
+    }
+    //Método para devolver el total de usuarios únicos
+
+}