diff --git a/pom.xml b/pom.xml
index 8169ff7..3eb4052 100644
--- a/pom.xml
+++ b/pom.xml
@@ -10,9 +10,11 @@
   <name>big-data</name>
   <url>http://maven.apache.org</url>
 
-  <properties>
-    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-  </properties>
+    <properties>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <maven.compiler.source>16</maven.compiler.source>
+        <maven.compiler.target>16</maven.compiler.target>
+    </properties>
 
   <dependencies>
       <dependency>
@@ -26,5 +28,20 @@
           <version>4.7</version>
           <scope>test</scope>
       </dependency>
+      <dependency>
+          <groupId>org.slf4j</groupId>
+          <artifactId>slf4j-api</artifactId>
+          <version>1.7.32</version>
+      </dependency>
+      <dependency>
+          <groupId>org.slf4j</groupId>
+          <artifactId>slf4j-simple</artifactId>
+          <version>1.7.5</version>
+      </dependency>
+      <dependency>
+          <groupId>com.google.guava</groupId>
+          <artifactId>guava</artifactId>
+          <version>31.0.1-jre</version>
+      </dependency>
   </dependencies>
 </project>
diff --git a/src/main/java/nearsoft/academy/bigdata/recommendation/MovieRecommender.java b/src/main/java/nearsoft/academy/bigdata/recommendation/MovieRecommender.java
new file mode 100644
index 0000000..1f194e3
--- /dev/null
+++ b/src/main/java/nearsoft/academy/bigdata/recommendation/MovieRecommender.java
@@ -0,0 +1,135 @@
+package nearsoft.academy.bigdata.recommendation;
+
+import com.google.common.collect.BiMap;
+import com.google.common.collect.HashBiMap;
+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.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.BufferedReader;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileWriter;
+import java.io.IOException;
+import java.io.InputStreamReader;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Objects;
+import java.util.stream.Collectors;
+import java.util.zip.GZIPInputStream;
+
+public class MovieRecommender {
+
+    private static final Logger log = LoggerFactory.getLogger(MovieRecommender.class);
+
+    public static final String ROOT_PATH = System.getProperty("user.dir");
+    private final String CSV_PATH = ROOT_PATH + "/src/main/resources/movies.csv";
+
+    private final Map<String, Long> users;
+    private final BiMap<String, Long> movies;
+
+    private Long totalReviews;
+    private static final Integer RECORDS = 1000000;
+    private static final Double THRESHOLD = 0.1;
+    private static final Integer RECOMMENDATIONS_NUM = 3;
+
+    public MovieRecommender(String filePath) {
+        users = new HashMap<>();
+        movies = HashBiMap.create();
+        totalReviews = 0L;
+        init(filePath);
+    }
+
+    public void init(String filePath){
+        try {
+            parseToCSV(readGZIPFile(filePath));
+        } catch (IOException e) {
+            log.error(e.getMessage());
+        }
+    }
+
+    public BufferedReader readGZIPFile(String filePath) throws IOException {
+        var fileInputStream = new FileInputStream(filePath);
+        var gzipInputStream = new GZIPInputStream(fileInputStream);
+        var inputStreamReader = new InputStreamReader(gzipInputStream);
+        return new BufferedReader(inputStreamReader);
+    }
+
+    public void parseToCSV(BufferedReader bufferedReader) throws IOException {
+
+        log.info("Parsing file to CSV");
+
+        var fileWriter = new FileWriter(CSV_PATH);
+
+        String userID = null, movieID = null, score = null;
+
+        String currentLine;
+        while ((currentLine = bufferedReader.readLine()) != null) {
+            if (currentLine.contains(":")) {
+
+                var field = currentLine.split(":", 2);
+                var fieldKey = field[0];
+                var fieldValue = field[1].trim();
+
+                switch (fieldKey) {
+                    case "product/productId" -> {
+                        movieID = fieldValue;
+                        var movieNum = (long) (movies.size() + 1);
+                        movies.putIfAbsent(movieID, movieNum);
+                    }
+                    case "review/userId" -> {
+                        userID = fieldValue;
+                        var userNum = (long) (users.size() + 1);
+                        users.putIfAbsent(userID, userNum);
+                    }
+                    case "review/score" -> {
+                        score = fieldValue;
+                        totalReviews++;
+                    }
+                }
+
+                if (Objects.nonNull(userID) && Objects.nonNull(movieID) && Objects.nonNull(score)) {
+                    fileWriter.write(String.format("%s,%s,%s\n", users.get(userID), movies.get(movieID), score));
+                    userID = movieID = score = null;
+                    if (totalReviews % RECORDS == 0){
+                        log.info(String.format("Parsed %d lines", totalReviews));
+                    }
+                }
+
+            }
+        }
+        fileWriter.close();
+
+        log.info(String.format("File parsed. Total lines %d", totalReviews));
+    }
+
+    public long getTotalReviews() {
+        return totalReviews;
+    }
+
+    public long getTotalProducts() {
+        return movies.size();
+    }
+
+    public long getTotalUsers() {
+        return users.size();
+    }
+
+    public List<String> getRecommendationsForUser(String userID) throws IOException, TasteException {
+        var file = new File(CSV_PATH);
+        var dataModel = new FileDataModel(file);
+        var userSimilarity = new PearsonCorrelationSimilarity(dataModel);
+        var neighborhood = new ThresholdUserNeighborhood(THRESHOLD, userSimilarity, dataModel);
+        var recommender = new GenericUserBasedRecommender(dataModel, neighborhood, userSimilarity);
+        return recommender.recommend(users.get(userID), RECOMMENDATIONS_NUM)
+                .stream()
+                .map(movie -> movies.inverse().get(movie.getItemID()))
+                .collect(Collectors.toList());
+    }
+
+}
diff --git a/src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommenderTest.java b/src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommenderTest.java
index 0d0b1fe..2cdbb48 100644
--- a/src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommenderTest.java
+++ b/src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommenderTest.java
@@ -6,6 +6,7 @@
 import java.io.IOException;
 import java.util.List;
 
+import static nearsoft.academy.bigdata.recommendation.MovieRecommender.ROOT_PATH;
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertThat;
 import static org.junit.matchers.JUnitMatchers.hasItem;
@@ -15,7 +16,7 @@ public class MovieRecommenderTest {
     public void testDataInfo() throws IOException, TasteException {
         //download movies.txt.gz from 
         //    http://snap.stanford.edu/data/web-Movies.html
-        MovieRecommender recommender = new MovieRecommender("/path/to/movies.txt.gz");
+        MovieRecommender recommender = new MovieRecommender(ROOT_PATH + "/src/main/resources/movies.txt.gz");
         assertEquals(7911684, recommender.getTotalReviews());
         assertEquals(253059, recommender.getTotalProducts());
         assertEquals(889176, recommender.getTotalUsers());