diff --git a/pom.xml b/pom.xml
index 8169ff7..7826f26 100644
--- a/pom.xml
+++ b/pom.xml
@@ -9,11 +9,23 @@
 
   <name>big-data</name>
   <url>http://maven.apache.org</url>
-
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <configuration>
+                    <source>1.8</source>
+                    <target>1.8</target>
+                </configuration>
+            </plugin>
+        </plugins>
+    </build>
   <properties>
     <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+      <maven.compiler.source>1.8</maven.compiler.source>
+      <maven.compiler.target>1.8</maven.compiler.target>
   </properties>
-
   <dependencies>
       <dependency>
           <groupId>org.apache.mahout</groupId>
diff --git a/src/data/movies.txt.gz b/src/data/movies.txt.gz
new file mode 100644
index 0000000..020e8a6
Binary files /dev/null and b/src/data/movies.txt.gz differ
diff --git a/src/main/java/movierec/MovieRecommender.java b/src/main/java/movierec/MovieRecommender.java
new file mode 100644
index 0000000..260eb45
--- /dev/null
+++ b/src/main/java/movierec/MovieRecommender.java
@@ -0,0 +1,125 @@
+// this class works with the src/data/movies.txt.gz compressed file as input
+// the src/data/movies.txt.gz current file is a provisional one for storage reasons
+// so, in order to pass the test properly, it needs to be replaced with the original 3+ GB file
+// ... with the correct file, this class generates an intermediate CSV file with clean data, 
+// which is about 150 MB big
+
+package movierec;
+
+import java.io.*;
+import java.util.zip.GZIPInputStream;
+import java.util.ArrayList;
+import java.util.Hashtable;
+import java.util.List;
+
+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;
+
+public class MovieRecommender {
+	int totalProducts;
+    int totalUsers;
+    int totalReviews;
+    // keep hash tables to be able to 'translate' between numeric and alphanum. IDs
+    Hashtable<String,Integer> products;
+    Hashtable<String,Integer> users;
+    Hashtable<Integer,String> productsByID;
+    String myPath;
+    String output;
+    
+    public MovieRecommender(String pathTidyFile) throws Exception {
+    	this.totalProducts = 0;
+        this.totalUsers = 0;
+        this.totalReviews = 0;
+        this.products = new Hashtable<String,Integer>();
+        this.users = new Hashtable<String,Integer>();
+        this.productsByID = new Hashtable<Integer,String>();
+        this.myPath = pathTidyFile;
+        this.output = "src/data/nice_data.csv";
+    	dataWrangling();
+    }
+    
+    // method for cleaning and re-arranging the data so that it is a valid input 
+    // for the mahout FileDataModel class
+    public void dataWrangling () throws IOException {
+		InputStream gzipStream = new GZIPInputStream(new FileInputStream(this.myPath));
+        BufferedReader buffered = new BufferedReader(new InputStreamReader(gzipStream));
+        FileWriter csvWriter = new FileWriter(this.output);
+        
+        String auxLine = "";
+        String productStr = "product/productId: ";
+        String userStr = "review/userId: ";
+        String scoreStr = "review/score: ";
+        String user = "";
+        String product = "";
+        String score = "";
+        
+		while (auxLine != null) {
+			if (auxLine.contains(userStr)) {
+				user = auxLine.split(" ")[1];
+				if(this.users.get(user) == null ) {
+					this.totalUsers ++;
+					this.users.put(user,this.totalUsers);
+				}
+			}
+			if (auxLine.contains(productStr)) {
+				product = auxLine.split(" ")[1];
+				if(this.products.get(product) == null ) {
+					this.totalProducts ++;
+					this.products.put(product,this.totalProducts);
+					this.productsByID.put(this.totalProducts,product);
+				}
+			}
+			if (auxLine.contains(scoreStr)) {
+				score = auxLine.split(" ")[1];
+				this.totalReviews ++;
+			}
+			if (user != "" && product != "" && score != "") {
+				csvWriter.write(this.users.get(user) + "," + this.products.get(product) + "," + score + "\n");
+				user = "";
+				product = "";
+				score = "";
+			}
+			auxLine = buffered.readLine();
+		}
+		
+		buffered.close();
+		csvWriter.close();
+
+    }
+    
+    // this method gets 3  item recommendations as output for a given user as input
+    public List<String> getRecommendationsForUser(String userID) throws IOException, TasteException{
+    	DataModel model = new FileDataModel(new File(this.output));
+    	UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
+    	UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
+    	UserBasedRecommender recommender = 
+  			  new GenericUserBasedRecommender(model, neighborhood, similarity);
+    	List<String> recommendations = new ArrayList<String>();
+    	for (RecommendedItem recommendation : recommender.recommend(this.users.get(userID), 3)) {
+    		recommendations.add(this.productsByID.get((int )(recommendation.getItemID())));
+    	}
+    	return recommendations;
+    	
+    }
+
+    public int getTotalReviews() {
+        return totalReviews;
+    }
+
+    public int getTotalProducts() {
+        return totalProducts;
+    }
+
+    public int getTotalUsers() {
+        return totalUsers;
+    }
+}
+
diff --git a/src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommenderTest.java b/src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommenderTest.java
index 0d0b1fe..8fb09e3 100644
--- a/src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommenderTest.java
+++ b/src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommenderTest.java
@@ -1,22 +1,24 @@
+// PLEASE CONSIDER THAT...
+// in order to pass the test properly, 
+// the current rc/data/movies.txt.gz file needs to be replaced with the original 3+ GB file
 package nearsoft.academy.bigdata.recommendation;
 
-import org.apache.mahout.cf.taste.common.TasteException;
+import movierec.MovieRecommender;
 import org.junit.Test;
-
-import java.io.IOException;
 import java.util.List;
-
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertThat;
 import static org.junit.matchers.JUnitMatchers.hasItem;
 
+
 public class MovieRecommenderTest {
     @Test
-    public void testDataInfo() throws IOException, TasteException {
+    public void testDataInfo() throws Exception {
         //download movies.txt.gz from 
         //    http://snap.stanford.edu/data/web-Movies.html
-        MovieRecommender recommender = new MovieRecommender("/path/to/movies.txt.gz");
-        assertEquals(7911684, recommender.getTotalReviews());
+
+        MovieRecommender recommender = new MovieRecommender("src/data/movies.txt.gz");
+        assertEquals(7911684, recommender.getTotalReviews()); // atributos clase principal
         assertEquals(253059, recommender.getTotalProducts());
         assertEquals(889176, recommender.getTotalUsers());
 
@@ -24,7 +26,5 @@ public void testDataInfo() throws IOException, TasteException {
         assertThat(recommendations, hasItem("B0002O7Y8U"));
         assertThat(recommendations, hasItem("B00004CQTF"));
         assertThat(recommendations, hasItem("B000063W82"));
-
     }
-
-}
+}
\ No newline at end of file