Adding implementation for movie recommender

pom.xml

@@ -15,16 +15,51 @@
   </properties>
 
   <dependencies>
-      <dependency>
-          <groupId>org.apache.mahout</groupId>
-          <artifactId>mahout-core</artifactId>
-          <version>0.9</version>
-      </dependency>
-      <dependency>
-          <groupId>junit</groupId>
-          <artifactId>junit</artifactId>
-          <version>4.7</version>
-          <scope>test</scope>
-      </dependency>
-  </dependencies>
-</project>
+        <dependency>
+            <groupId>org.apache.mahout</groupId>
+            <artifactId>mahout-core</artifactId>
+            <version>0.9</version>
+        </dependency>
+
+        <dependency>
+            <groupId>junit</groupId>
+            <artifactId>junit</artifactId>
+            <version>4.7</version>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-simple</artifactId>
+            <version>1.7.12</version>
+        </dependency>
+        <dependency>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-jdk14</artifactId>
+            <version>1.5.6</version>
+            <scope>test</scope>
+        </dependency>
+
+        <dependency>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-jdk14</artifactId>
+            <version>1.5.6</version>
+            <scope>test</scope>
+        </dependency>
+
+
+    </dependencies>
+
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <version>3.8.0</version>
+                <configuration>
+                    <source>1.8</source>
+                    <target>1.8</target>
+                </configuration>
+            </plugin>
+        </plugins>
+    </build>
+</project>

src/main/java/nearsoft/academy/bigdata/recommendation/MovieRecommender.java

@@ -0,0 +1,113 @@
+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.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;
+
+import java.io.*;
+import java.nio.file.Files;
+import java.nio.file.Paths;
+import java.util.*;
+import java.util.zip.GZIPInputStream;
+
+public class MovieRecommender {
+
+    private int totalReviews = 0;
+    private int totalProducts = 0;
+    private int totalUsers = 0;
+    private float score = 0.0f;
+    private BiMap<String, Long> productsHash = HashBiMap.create();
+    private BiMap<String, Long> usersHash = HashBiMap.create();
+
+    public MovieRecommender(String path) throws IOException {
+        writeFile(path);
+    }
+
+    private void writeFile(String path) throws IOException {
+        boolean bandera = true;
+        Long thisProduct = 0l;
+        Long thisUser = 0l;
+        Files.deleteIfExists(Paths.get("Result.csv"));
+        File result = new File("Result.csv");
+        InputStream fileReader = new GZIPInputStream(new FileInputStream(path));
+        BufferedReader br = new BufferedReader(new InputStreamReader(fileReader));
+        FileWriter fileWriter = new FileWriter(result);
+        BufferedWriter bw = new BufferedWriter(fileWriter);
+        String line;
+        String[] sp;
+        Long identificadorUsuario = 0l;
+        Long identificadorProducto = 0l;
+        String key, value;
+        while((line = br.readLine()) != null) {
+            if (line.length() >= 0) {
+                sp = line.split(" ");
+                key = sp[0];
+                if (key.equals("product/productId:")) {
+                    value = sp[1];
+                    if (!productsHash.containsKey(value)){
+                        productsHash.put(value,identificadorProducto++);
+                        thisProduct = productsHash.get(value);
+                        this.totalProducts++;
+                    }else{
+                        thisProduct = productsHash.get(value);
+                    }
+                }else if (key.equals("review/userId:")){
+                    value = sp[1];
+                    if (!usersHash.containsKey(value)){
+                        usersHash.put(value, identificadorUsuario++);
+                        this.totalUsers++;
+                    }
+                    thisUser = usersHash.get(value);
+                }else if (key.equals("review/score:")){
+                    String score = sp[1];
+                    bw.write(thisUser + "," + thisProduct + "," + score + "\n");
+                    this.totalReviews ++;
+                }
+            }
+        }
+        br.close();
+        bw.close();
+    }
+
+    public int getTotalReviews() {
+        return this.totalReviews;
+    }
+
+    public int getTotalProducts() {
+        return this.totalProducts;
+    }
+
+    public int getTotalUsers() {
+        return this.totalUsers;
+    }
+
+    public List<String> getRecommendationsForUser(String id) throws IOException, TasteException {
+        DataModel model = new FileDataModel(new File("result.csv"));
+        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
+        UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
+        UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
+
+        Long userLongId = usersHash.get(id);
+
+        List<RecommendedItem> recommendations = recommender.recommend(userLongId, 3);
+        List<String> recommendationsAsStr = new ArrayList<>();
+        productsHash.inverse();
+
+
+        for (RecommendedItem recommendation: recommendations) {
+            recommendationsAsStr.add(productsHash.inverse().get(recommendation.getItemID()));
+        }
+        return recommendationsAsStr;
+    }
+}

src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommenderTest.java

@@ -13,9 +13,10 @@
 public class MovieRecommenderTest {
     @Test
     public void testDataInfo() throws IOException, TasteException {
-        //download movies.txt.gz from 
+        //download movies.txt.gz from
         //    http://snap.stanford.edu/data/web-Movies.html
-        MovieRecommender recommender = new MovieRecommender("/path/to/movies.txt.gz");
+        String test = System.getProperty("user.dir")+"/src/test/java/nearsoft/academy/bigdata/recommendation/movies.txt.gz";
+        MovieRecommender recommender = new MovieRecommender(test);
         assertEquals(7911684, recommender.getTotalReviews());
         assertEquals(253059, recommender.getTotalProducts());
         assertEquals(889176, recommender.getTotalUsers());
@@ -27,4 +28,4 @@ public void testDataInfo() throws IOException, TasteException {
 
     }
 
-}
+}